CN113057649B - Wireless physiological monitoring device and system - Google Patents

Abstract

The present disclosure relates to a cardiac monitoring system and methods of using such a system. Preferred embodiments detect and record cardiac information by a wearable device and then extract data features from the recorded cardiac information. The extracted data features can then be analyzed and used for clinical diagnosis.

Description

Wireless physiological monitoring device and system

The application is a divisional application of a patent application with the filing date of 2015, 10 months and 30 days, the filing number of 2015800593758 and the title of the invention being "wireless physiological monitoring device and system".

Cross Reference to Related Applications

This application claims benefit of U.S. provisional application entitled "Wireless physiological monitoring," U.S. Ser. No. 62/073,910, filed on 31/10/2014. The foregoing application is incorporated by reference herein in its entirety as if fully set forth herein. The benefit of priority of the above application is claimed under appropriate legal grounds, including but not limited to.

Technical Field

For the purposes of this disclosure, several aspects, advantages, and novel features of various embodiments are described herein. It is to be understood that not necessarily all such advantages may be achieved in accordance with any particular embodiment. Thus, various embodiments may be, or be implemented in a manner that achieves one advantage or group of advantages as taught herein without necessarily achieving other advantages as may be taught or suggested herein.

A system for inferring heart rate information from heartbeat time series information collected by a wearable sensor, and a system for selectively transmitting electrocardiogram signal data from a wearable sensor.

Background

Cardiac arrhythmias or arrhythmias may cause various types of symptoms such as loss of consciousness, palpitations, dizziness, and even death. Arrhythmias causing this symptom are often indicators of significant underlying cardiac disease. Because treatment with various procedures such as pacemaker implantation or percutaneous catheter ablation can successfully ameliorate these problems and prevent the appearance of significant symptoms and death, it is important to determine when such symptoms are due to abnormal heart rhythm. For example, monitors, such as electrocardiogram dynamic (Holter) monitors and similar devices, are currently being used to monitor heart rhythm.

Disclosure of Invention

Embodiments described herein relate to a physiological monitoring device that is continuously and comfortably worn by a human or animal subject for at least one week or more, and more typically two to three weeks or more. In one embodiment, the device is specifically designed to sense and record cardiac rhythm (e.g., electrocardiogram, ECG) data, although in various alternative embodiments one or more additional physiological parameters may be sensed and recorded. Such physiological monitoring devices may include a number of features that facilitate and/or enhance patient experience and make arrhythmia diagnosis more accurate and timely.

In some embodiments, an electronic device for monitoring physiological signals in a mammal comprises: at least two flexible wings extending laterally from the rigid housing, wherein the flexible wings comprise a first set of materials that enable the wings to conform to a surface of a mammal and the rigid housing comprises a second set of materials; a printed circuit board assembly housed within a rigid housing, wherein the rigid housing is configured to prevent deformation of the printed circuit board in response to movement of the mammal; at least two electrodes embedded within the flexible wings, the electrodes configured to provide conformal contact with a surface of the mammal and to detect physiological signals of the mammal; at least two electrode traces embedded within the wings and mechanically separated from the rigid housing, the electrode traces configured to provide conformal contact with a surface of the mammal and to transmit electrical signals from the electrodes to the printed circuit board assembly; and at least one hinge portion connecting the wing portion to the rigid housing, the hinge portion being configured to freely bend at a region where it engages the rigid housing.

In some embodiments, each wing portion may include an adhesive. In an embodiment, the electrodes may be in the same plane as the adhesive. In some embodiments, each wing includes at least one rim, wherein the rim is thinner than an adjacent portion of each wing. The rigid housing may further include a depression (double) configured to account for airflow between the rigid housing and the surface of the mammal. In some embodiments, the rim is configured to prevent a portion of the wing from loosening from a surface of the mammal. In some embodiments, electronics for monitoring a physiological system may include a measurement instrument configured to detect a motion signal on at least one axis. The measurement instrument may be an accelerometer that may be configured to detect motion signals in three axes.

In an embodiment, the motion signal may be collected in time with the physiological signal. In some embodiments, motion artifacts (artifacts) are identified when the physiological signal and the motion signal match. Further embodiments may require an event trigger coupled to the printed circuit board assembly. In some embodiments, the event trigger input is supported by a rigid housing to prevent mechanical stress on the printed circuit board when the trigger is activated, which in turn may reduce the source of artifacts in the recorded signal. The event trigger may be concave and larger than a human finger so that the event trigger is easily located. In some embodiments, the electrode traces are configured to minimize signal distortion during movement of the mammal. In certain embodiments, a gasket may be used as a means for sealable attachment to a rigid housing.

In some embodiments, a method for monitoring physiological signals in a mammal may comprise: attaching an electronic device to a mammal, wherein the device comprises: at least two electrodes configured to detect physiological signals from the mammal, at least one measurement instrument configured to detect secondary signals, and at least two electrode traces connected to the electrodes and the rigid housing; and comparing the physiological signal to the secondary signal to identify the artifact.

In some embodiments, identifying the artifact comprises a comparison between a frequency spectrum of the physiological signal and a frequency spectrum of the secondary signal. In an embodiment, the secondary signal comprises a motion signal that can be used to derive the activity and position of the mammal. In some embodiments, the secondary signals are collected on three axes. In some embodiments, three levels of signals may also be collected. In some embodiments, the secondary signal includes information about the connection between the electronic device and the mammal. In some embodiments, the secondary signal may be used to detect when the mammal is sleeping.

In some embodiments, a method for removing and replacing portions of a modular physiological monitoring device may include: applying the device to a mammal for a period of more than 7 days and collecting physiological data; detecting, using a device, a first set of physiological signals; removing the device from the surface of the mammal; removing the first component from the device; and incorporating the first component into a second physiological monitoring device configured to detect a second set of physiological signals.

In some embodiments, the first component is electrically connected to the other device component without the use of a permanent connection. In some embodiments, the device may further comprise a spring connection. In some embodiments, the first component may be protected from damage by a rigid housing for secondary use. In certain embodiments, the first component is secured within the device by a mechanism that is capable of re-securing the second component after removal of the first component.

Some embodiments may relate to a system that infers heart rhythm information from time series data of heart beat intervals as obtained from a consumer wearable product or medical device product. Another aspect relates to improvements to systems that are capable of inferring heart rate information in a more robust and/or timely manner through the use of additional data sources. Such additional data may include summary statistics or specific signal features derived from an ECG, user activity time series data derived from an accelerometer, information related to user status, or information related to recording date/time.

In some embodiments, a system for selectively transmitting electrocardiographic signal data from wearable medical sensors, where QRS refers to three fiducial points of ECG recording at ventricular depolarization, may comprise:

a. wearable medical sensor including QRS detector that produces real-time estimates of the position of each R peak in an ECG

b. Transmitting the R-R interval time series from the sensor to the smart phone or internet connected gateway device together with the start time stamp according to a predetermined schedule

c. Transmitting R-R interval time series and start time stamp from smart phone or internet connected gateway device to server

d. Server-side algorithm for deducing most probable rhythm and starting point time-ending point time thereof from R-R interval time sequence data

e. Filtering the list of inferred rhythms according to specific filtering criteria such that only inferred rhythms matching a given criteria remain after filtering

f. Transmitting the start time-end time of each rhythm remaining after filtering from a server to a smart phone or an internet connected gateway device

g. Transmitting the start time-end time of each rhythm remaining after filtering from a smartphone or internet connected gateway device to a wearable sensor

h. Transmitting the portion of the recorded ECG corresponding to each start time-end time pair from the sensor to a smart phone or internet connected gateway device

i. The portion of the recorded ECG corresponding to each start time-end time pair is transmitted from the smartphone or internet connected gateway device to the server.

The rhythm filtering criteria may be specified by a physician or other medical professional prior to the patient's use of the wearable sensor. In other embodiments, the rhythm filter criteria are dynamic and may be updated during use of the system according to predetermined rules. In some embodiments, these predetermined rules may describe adjustments to the filtering criteria based on previous findings during use of the system. In some embodiments, the start and end times of each inferred rhythm may be adjusted such that the resulting duration for each rhythm is less than a given maximum allowable duration. The calculated confidence measure may be an input to the rhythm filtering criteria. In some embodiments, the system includes inferring heart rate information from the R-R interval time series data. In some embodiments, the rhythm inference system is implemented as a cloud service accessible via an API.

In some embodiments, the heart rhythm inference system is provided by a software library that may be incorporated into a stand-alone application. R-R interval values may be estimated from the photoplethysmograph signal.

In some embodiments of the method for inferring heart rhythm information, the heart rhythm inference system calculates a confidence score for each type of heart rhythm, the method comprising:

a. calculating the frequency and duration of each rhythm type inferred from the collection of R-R interval time series data for a given user

b. Estimating confidence statistics for each rhythm type based on inferred frequencies and durations of entire collected rhythms for a given user's R-R interval time series

c. Evaluating whether the confidence statistic for each inferred rhythm exceeds a predetermined threshold

d. Rhythm information is returned to the calling software only for those inferred rhythms for which the confidence statistic exceeds the threshold.

In some embodiments, the cardiac rhythm inference system receives additional data sources including one or more of:

e. user activity time series data measured by an accelerometer

f. Information about specific date and time recorded for each R-R interval time series

g. Information about the user's age, gender, clinical monitoring indicators, pre-existing medical conditions, drug information, and medical history

ECG signal characteristics and summary statistics, such as the mean, median, standard deviation or sum of ECG signal sample values over a given time period

i. Indications provided by the measuring device, e.g. confidence ratings for the quality of the heartbeat estimate for each beat or for successive time periods

j. Internal beat interval measurement.

In an embodiment, a system for monitoring cardiac signal data includes:

a wearable medical sensor configured to detect a cardiac signal from the mammal and to estimate an R-peak location within the cardiac signal;

wherein the wearable medical sensor is configured to transmit the R-R interval time series and the time stamp to an intermediate device, the intermediate device configured to further transmit the R-R interval time series and the time stamp to a server;

wherein the server is configured to infer a most likely rhythm and its start time-end time from the R-R interval time series and the time stamp, the server configured to filter the most likely rhythm into the filtered data set according to a first criterion;

wherein the server is configured to transmit the filtered data set back to the wearable sensor through the intermediary device; and

wherein the sensor transmits full resolution cardiac signals to the server for a time period surrounding each of the filtering events.

In some embodiments, a system for monitoring cardiac signal data comprises:

a server configured to communicate with a wearable sensor, the wearable sensor configured to detect a cardiac signal from the mammal and to estimate an R-peak location within the cardiac signal;

wherein the wearable sensor is configured to transmit the R-R interval time series and the time stamps to the server;

wherein the server is configured to infer a most likely rhythm and its start time-end time from the R-R interval time series and the time stamp, the server configured to filter the most likely rhythm into the filtered data set according to a first criterion; and

wherein the server is configured to transmit the summary of filtered data.

In a particular embodiment, a server for monitoring cardiac signal data includes:

a portal (portal) configured to communicate with a wearable sensor configured to detect a cardiac signal from the mammal and to estimate an R-peak position within the cardiac signal, wherein the wearable sensor is configured to transmit the R-R interval time series and the timestamp to an intermediate device, the intermediate device configured to further transmit the R-R interval time series and the timestamp to a server;

a processor configured to infer a most likely rhythm and its start time-end time from the R-R interval time series and the time stamp, the processor configured to filter the most likely rhythm into a filtered data set according to a first criterion; and

wherein the server is configured to transmit the summary of the filtered data set.

In an embodiment, a non-transitory storage medium having stored thereon computer-executable instructions readable by a computing system comprising one or more computing devices, wherein the computer-executable instructions are executable on the computing system to cause the computing system to perform operations comprising: receiving, by a computing system via a communication link, physiological sensor data generated by a patient monitoring device, the physiological sensor data associated with a first patient; analyzing, by the computing system, the physiological sensor data to determine whether one or more points in the physiological data are likely to indicate one or more predetermined sets of conditions; and after determining that at least one of the one or more points in the physiological data is likely to indicate at least one of the one or more predetermined sets of conditions, generating, by the computing system, an electronic data package for transmission to the patient monitoring device, the electronic data package including location data regarding at least one of the one or more points in the physiological sensor data that is likely to indicate at least one of the one or more predetermined sets of conditions.

In some embodiments, the physiological sensor data may include samples of interval data measured from the recorded signal data, the samples of interval data having a data size smaller than a data size of the recorded signal data.

In a particular embodiment, a system for monitoring physiological signals in a mammal can include: a wearable adhesive monitor configured to detect and record cardiac rhythm data from the mammal, the wearable adhesive monitor configured to extract features from the cardiac rhythm data; and wherein the wearable adhesive monitor is configured to transmit the feature to the processing device, the processing device configured to analyze the feature, identify the location of interest, and transmit the location of interest back to the wearable adhesive monitor.

In some embodiments, a system for evaluating physiological sensor data from a patient monitoring device includes: a computer processor and a non-transitory computer-readable medium in combination with the computer processor configured to provide a program comprising a set of instructions stored on a first server, the set of instructions being executable by the computer processor and further configured to execute a sensor data inference module of the program, the sensor data inference module of the program storing instructions to: receiving physiological sensor data generated by a patient monitoring device, the physiological sensor data associated with a first patient; analyzing the physiological sensor data to determine whether one or more points in the physiological data are likely to indicate one or more predetermined sets of conditions; and after determining that at least one of the one or more points in the physiological data is likely indicative of at least one of the one or more predetermined sets of conditions, generating an electronic data packet for transmission to the patient monitoring device, the electronic data packet including location data regarding at least one of the one or more points in the physiological sensor data that is likely indicative of at least one of the one or more predetermined sets of conditions.

In some embodiments, the computerized method may comprise: accessing computer-executable instructions from at least one computer-readable storage medium; and executing computer-executable instructions, thereby causing computer hardware comprising at least one computer processor to perform operations comprising: receiving, by a server computer via a communication link, physiological sensor data generated by a patient monitoring device, the physiological sensor data associated with a first patient; analyzing, by the server computer, the physiological sensor data to determine whether one or more points in the physiological data are likely to indicate one or more predetermined sets of conditions; after determining that at least one of the one or more points in the physiological data is likely indicative of at least one of the one or more predetermined sets of conditions, generating, by the server computer, an electronic data packet for transmission to the patient monitoring device, the electronic data packet including location data regarding the at least one of the one or more points in the physiological sensor data that is likely indicative of the at least one of the one or more predetermined sets of conditions.

These and other aspects and embodiments of the invention are described in more detail below with reference to the drawings.

Drawings

Fig. 1A and 1B are perspective, cross-sectional and exploded cross-sectional views, respectively, of a physiological monitoring device according to one embodiment.

Fig. 2A and 2B are top and bottom perspective views, respectively, of a printed circuit board assembly of a physiological monitor device according to one embodiment.

Fig. 3A, 3B, 3C, 3D, and 3E are perspective and exploded views of a flexible body and a gasket of a physiological monitoring device according to one embodiment.

FIG. 4 is an exploded view of a rigid housing of a physiological monitoring device according to one embodiment.

Fig. 5A and 5B provide perspective views of a battery holder of a physiological monitoring device according to one embodiment.

Fig. 6A and 6B are cross-sectional views of a physiological monitoring device according to one embodiment.

FIG. 7 is an exploded view of a physiological monitoring device including a plurality of selectable items according to one embodiment.

Fig. 8A and 8B are perspective views of two persons wearing physiological monitoring devices illustrating how the devices bend to conform to body movement and position, according to one embodiment.

Fig. 9A, 9B, 9C, 9D, 9E and 9F illustrate various steps of applying a physiological monitor to a patient's body according to one embodiment.

Fig. 10 illustrates a schematic diagram of an embodiment of a rhythm inference service.

FIG. 11 is a schematic diagram of an embodiment of a system for extracting and transmitting data features from a physiological monitor.

FIG. 12 is a schematic diagram of an embodiment of a system for extracting and transmitting data features from a physiological monitor using a transmission device.

FIG. 13 is a schematic diagram of an embodiment of a physiological monitoring system utilizing additional data channels.

FIG. 14 is a schematic diagram of an embodiment of a physiological monitoring system including a data filter.

Fig. 15 is a schematic diagram of an embodiment of a wearable device system.

Fig. 16 is a schematic diagram of an embodiment of a symptomatic delivery system.

Fig. 17 is a schematic diagram of an embodiment of an asymptomatic delivery system.

FIG. 18 is a schematic diagram of an embodiment of a computer network system.

FIG. 19 is a schematic diagram of an embodiment of a programming and distribution module.

Detailed Description

The following description relates to some of the various embodiments. However, the described embodiments may be implemented and/or varied in many different ways. For example, the described embodiments may be implemented in any suitable apparatus, device or system for monitoring any one of a number of physiological parameters. For example, the following discussion focuses primarily on long-term patch-based heart rhythm monitoring devices. In an alternative embodiment, physiological monitoring devices may be used, for example for pulse oximetry and diagnosis of obstructive sleep apnea. The method of using the physiological monitoring device can also vary. In some cases, the device may be worn for a week or less, while in other cases, the device may be worn for at least seven days and/or more, such as between fourteen days and twenty days, or even longer. Many other alternative embodiments and applications of the described techniques are possible. Accordingly, the following description is provided for illustrative purposes only. Throughout the specification, reference may be made to the term "conformal". One skilled in the art will appreciate that the term "conformal" as used herein refers to a relationship between surfaces or structures in which a first surface or structure conforms to the contours of a second surface or structure.

Since arrhythmias or arrhythmias can often be due to other less severe causes, a key challenge is determining when these symptoms are due to arrhythmias. Often, arrhythmias occur infrequently and/or sporadically, making rapid and reliable diagnosis difficult. As described above, currently, heart rhythm monitoring is mainly achieved by using devices such as holter monitors that utilize short duration (less than 1 day) electrodes affixed to the chest. Wires connect the electrodes to a recording device, typically worn on a belt. The electrodes need to be changed daily and the wires are cumbersome. The memory and recording time of the device is also limited. The worn device interferes with the movement of the patient and often fails to perform some activities while monitoring such as bathing. Further, holter monitors are capital equipment with limited availability, which often results in supply limitations and corresponding testing delays. These limitations severely hamper the diagnostic utility of the device, the compliance of the patient using the device, and the possibility of capturing all important information. The lack of compliance and the disadvantages of the devices often result in the need for additional devices, subsequent monitoring or other testing to make the correct diagnosis.

Current methods that correlate symptoms with the occurrence of arrhythmias, including the use of cardiac rhythm monitoring devices such as holter monitors and cardiac event recorders, are often inadequate to achieve accurate diagnosis. In fact, the holter monitor has been shown to not lead to diagnosis for up to 90% of the time (DE Ward et al, in 1980, "Biometric Patient Monitoring", volume 7, entitled "evaluation of Diagnostic Value of 24-Hour Ambulatory electrocardiogram Monitoring" (Assessment of the Diagnostic Value of 24-Hour Ambulatory electrocardiogram Monitoring) ").

In addition, the medical procedure of actually obtaining a heart rhythm monitoring device and starting the monitoring is often very complicated. There are typically many steps involved in ordering, tracking, monitoring, retrieving and analyzing data from such monitoring devices. In most cases, the cardiac monitoring devices used today are predetermined by cardiologists or cardiac Electrophysiologists (EPs) rather than by the patient's Primary Care Physician (PCP). This is of great significance because PCP is often the first physician to see a patient and to determine that the patient's symptoms may be due to an arrhythmia. After the patient has seen the PCP, the PCP will reserve the patient for a cardiologist or EP. This appointment is typically weeks after the initial visit to the PCP, which itself causes delays in the potential diagnosis and increases the likelihood that an arrhythmic event will occur that is not diagnosed. The heart rhythm monitoring device will typically be scheduled when the patient ends up seeing the cardiologist or EP. The monitoring period may last from 24 hours to 48 hours (holter monitor) or up to a month (cardiac event monitor or mobile telemetry device). Once monitoring is complete, the patient typically must return the device to the clinic, which may be inconvenient in itself. After the data has been processed by a technician at the monitoring company or hospital or office site, the report will eventually be sent to a cardiologist or EP for analysis. This complex process results in fewer patients receiving rhythm monitoring than would ideally receive rhythm monitoring.

To address some of these issues with cardiac monitoring, the assignee of the present application developed various embodiments of small, long-term, wearable physiological monitoring devices. One embodiment of the device is

And (3) pasting. Various embodiments are also described, for example, in U.S. Pat. nos. 8,150,502, 8,160,682, 8,244,335, 8,560,046, and 8,538,503, the entire disclosures of which are incorporated herein by reference. The patch-based physiological monitors described in the above references are typically comfortably fitted to the chest of a patient and are designed to be worn for at least one week and typically two to three weeks. The monitor continuously detects and records heart rhythm signal data while the device is worn, and such heart rhythm data is then available for processing and analysis.

These smaller, long-term, patch-based physiological monitoring devices offer many advantages over prior art devices. At the same time, further improvements are desired. One of the most significant areas for improvement is to provide more timely notification of critical arrhythmias to manage clinicians. The hallmark of these initial embodiments is that-for performance, compliance and cost reasons-the device records information only during extended wear, with analysis and reporting occurring after the recording is completed. Thus, a desirable improvement would be the ability to increase the real-time or timely analysis of the collected rhythm information. While diagnostic monitors exist today that have such timely reporting capabilities, they require frequent charging or replacement of one or more electrical components of the system. These behaviors are associated with decreased patient compliance and decreased diagnostic rates. Thus, one key area of improvement is the development of physiological detectors that can combine long-term recording with timely reporting without requiring battery charging or replacement.

Patient compliance and device adherence performance are two factors that control ECG recording duration and thus diagnostic rate. Compliance can be improved by improving the wearing experience of the patient, which is affected by wearing comfort, appearance of the device, and the degree to which the device impedes normal activities of daily life. In view of the greater diagnostic rate and thus value provided by longer ECG recordings, improvements in device adherence and patient compliance are desired.

Signal quality is important throughout the wear period but may be more important in the case of areas where the patient marks the record indicating symptomatic clinical significance. The recording of the indicia is most easily accomplished by a trigger located on the exterior surface of the device. However, since the trigger may be part of the skin contact platform with integrated electrodes, the patient may introduce significant motion artifacts when the trigger is felt. A desirable device improvement would be a symptom trigger that can be activated with minimal added motion artifact.

Further, it is desirable that the device be simple and economical to manufacture, thereby achieving scalability and higher quality in manufacturing due to repeatability in processes. The simplicity of manufacture may also result in easy disassembly, which enables efficient recovery of the printed circuit board for reuse in quality control in another device. Effective reuse of such expensive components may be important to reduce the cost of the diagnostic monitor.

There are still longer duration and lower cost solutions that may be valuable complementary clinical scenarios to the combination of cardiac dynamics monitoring options. The inspiration for a potential solution to these needs can be found in continuous heart rate sensing functionality that is increasingly being incorporated into a variety of consumer health and fitness products, including smart watches and wearable fitness bands. While continuous heart rate data may be used to provide information to users about their general fitness level, using this data to provide meaningful information about their health and wellness is more challenging and valuable. For example, the ability to detect potential arrhythmias from continuous heart rate data would enable consumer devices incorporating heart rate sensing functionality to be used as potential screening tools for early detection of cardiac abnormalities. Such a method may be of clinical value in providing a long-term, economical screening method for at-risk populations such as heart failure patients at risk for atrial fibrillation. Alternatively, such monitoring methods may facilitate long-term titration of therapeutic drug doses to ensure effectiveness while reducing side effects, for example, in the management of paroxysmal atrial fibrillation. In addition to arrhythmia detection, appropriate analysis of heart rate information may also enhance understanding of sleep and stress applications.

Long term dynamic monitoring using physiological devices such as patches has some clinical applications, particularly when timely information about the observed arrhythmia occurrence and duration can be provided during monitoring. Effective detection of Atrial Fibrillation (AF) remains the greatest monitoring need in terms of prevalence, particularly driven by aging population. This need is not only evident for patients presenting with symptoms, but-in view of the increased risk of stroke associated with such arrhythmias-for a more extensive population-based monitoring of asymptomatic AF in individuals at risk due to one or more factors of advanced age, the presence of chronic diseases such as heart disease, or even the occurrence of surgery. For the latter group, both perioperative and postoperative monitoring may be clinically valuable not only for arrhythmia-preventing procedures (e.g., MAZE ablation procedures or hybrid endoscopic and epicardial procedures, both for treating AF), but also for general procedures involving anesthesia. For some applications, the goal of dynamic monitoring of atrial fibrillation will sometimes focus on a simple binary question as to whether yes or no-AF occurs within a given time period. For example, monitoring a patient after an ablation procedure will typically seek to confirm success, typically defined as a complete lack of AF occurrence. Also, monitoring patients after stroke will be primarily concerned with assessing the presence of atrial fibrillation.

However, even in these cases, if AF occurs, additional aspects are evaluated to better characterize the occurrence such as daily burden (percentage of time in AF per day) and seizure duration (e.g., as a histogram of seizure duration, or as a percentage of seizures that last beyond a specified limit, such as six minutes), both in absolute terms or in comparison to previous benchmarks (e.g., from baseline, preoperative monitoring results) may be clinically meaningful. Indeed, measuring daily AF burden, assessing AF episode duration, examining AF occurrence during sleep and wake, and assessing the presence of AF in response to the extent of patient's physical movement may be important in a variety of clinical scenarios including assessing the effectiveness of drug therapy for such arrhythmias.

Making such information available in time during monitoring may allow the administering physician to iteratively titrate the treatment, for example, by adjusting the dose and frequency of a novel oral anticoagulant drug (NOAC) until administration is optimized. Another example of such a mode of management is-directly through the device via an audible or vibration-based alert, through notification from an application connected to the device or through telephone, email or text messaging communication from a managing clinician-allowing asymptomatic AF to be notified to the patient for AF management to apply a "dunk in the pocket" in time.

The subject of timely management and/or intervention is certainly evident in the case of clinically significant arrhythmias observed, e.g., asymptomatic second and complete cardiac blockages, prolonged pauses, high velocity supraventricular tachycardia, long term ventricular tachycardia and ventricular fibrillation. For example, a clinical situation where prolonged pauses or complete heart block causes syncope is a particularly important situation, and the availability of timely and reliable monitoring methods can reduce or even eliminate the need for in-patient monitoring of at risk patients. The subject matter can also be extended to more subtle morphological changes, for example, QT prolongation in response to drugs, which have been shown to have significant cardiac safety implications. For example, timely understanding of such prolongation may lead to early termination of clinical studies evaluating drug safety and efficacy or, alternatively, to adjustment of dose or frequency as a means of eliminating observed prolongation.

Physiological monitoring device

Referring to fig. 1A and 1B, a perspective cutaway and an exploded cutaway of one embodiment of a physiological monitoring device 100 are provided. As shown in fig. 1A, the physiological monitoring device 100 can include a flexible body 110 coupled with a watertight rigid housing 115. The flexible body 110 (which may be referred to as a "flexible substrate" or "flexible construction") generally includes two wings 130, 131 extending laterally from the rigid housing 115 and two flexible electrode traces 311, 312 each embedded in one of the wings 130, 131. Each electrode trace 311, 312 is coupled with a flexible electrode (not visible in fig. 1A) on the bottom surface of the flexible body 110. The electrodes are configured to sense a heart rhythm signal from a patient to which monitoring device 100 is attached. The electrode traces 311, 312 then transmit these signals to electronics (not visible in fig. 1A) housed in the rigid housing 115. The rigid housing 115 also typically contains a power source, such as one or more batteries.

The combination of a highly flexible body 110 comprising flexible electrodes and electrode traces 311, 312 and a very rigid housing 115 may provide some advantages. A key advantage is high fidelity signal capture. The highly conformal and flexible wings 130, 131, electrodes and traces 311, 312 limit the transmission of external energy to the electrode-skin interface. For example, if motion is applied to the rigid housing 115, a system that conformally adheres to the skin limits the extent to which the motion affects the monitored signal. The flexible electrode traces 311, 312 may generally help provide conformal contact with the subject's skin and may help prevent the electrodes 350 (the electrodes 350 are not visible in fig. 1, but are visible in fig. 6A described below) from peeling or falling off the skin, thereby providing strong motion artifact suppression and better signal quality by minimizing stress transfer to the electrodes 350. Further, the flexible body 110 includes a configuration and various features that facilitate comfortable wearing of the device 100 by a patient for fourteen (14) days or more without removal. The rigid housing 115, which is not typically attached to the patient in the embodiments described herein, includes features that make the device 100 comfortable. Hinge portion 132 is a relatively thin, even more flexible portion of flexible body 110. They allow the flexible body 110 to freely bend at the region where it is joined to the rigid housing 115. This flexibility enhances comfort because the housing 115 may be free to move away from the patient's skin as the patient moves. The electrode traces 311, 312 are also very thin and flexible to allow patient movement without signal distortion.

Referring now to fig. 1B, a partially exploded view of the physiological monitoring device 100 illustrates in more detail the components that make up the rigid housing 115 and are contained within the rigid housing 115. In this embodiment, the rigid housing 115 includes an upper housing member 140 that is removably coupled to a lower housing member 145. Upper and lower washers 370 and 360 (not visible in fig. 1B, but directly below upper washer 370) are sandwiched between upper and lower housing members 140 and 145. When assembled, the gaskets 370, 360 help to waterproof the rigid housing member 115. Some components of monitoring device 100 may be housed between upper housing member 140 and lower housing member 145. For example, in one embodiment, the housing 115 may contain portions of the flexible body 110, a Printed Circuit Board Assembly (PCBA) 120, a battery holder 150, and two batteries 160. Printed circuit board assembly 120 is positioned within housing 115 to contact electrode traces 311, 312 and battery 160. In various embodiments, one or more additional components may be contained within the rigid housing 115 or attached to the rigid housing 115. Some of these optional components are further described below with reference to the figures.

The battery holder 150 according to various alternative embodiments may hold two batteries (as in the illustrated embodiment), one battery, or more than two batteries. In other alternative embodiments, other power sources may be used. In the illustrated embodiment, the battery holder 150 includes a plurality of holding pieces 153 for holding the battery 160 in the holder 150. In addition, battery holder 150 includes a plurality of feet 152 that establish proper spacing of battery 160 from the surface of PCBA120 and ensure proper contact with spring fingers 235 and 236. Spring fingers 235 and 236 are used in this embodiment, rather than soldering battery 160 to PCBA 120. While soldering may be used in alternative embodiments, one advantage of spring fingers 235 and 236 is that they allow battery 160 to be removed from PCBA120 and holder 150 without damaging either of these components, thereby allowing multiple re-uses of both. Eliminating the welded connection also simplifies and speeds up assembly and disassembly of the monitoring device 100.

In some embodiments, the upper housing member 140 may serve as a patient event trigger. It is generally advantageous for the patient to be able to register (e.g., log into the memory of the device) with the device 100 any cardiac events that the patient perceives when the patient is wearing the physiological monitoring device 100 for cardiac rhythm monitoring. For example, if the patient feels that he/she thinks of an episode of arrhythmia, the patient may somehow trigger the device 100 and thus provide a record of the perceived event. In some embodiments, the triggering of an event perceived by the patient may initiate the transmission of data associated with the triggering event. In some embodiments, the triggering of a sensory event may simply mark a continuous recording with the location of the trigger event. In some embodiments, both the transmission of the associated data and the marking of the continuous recording may occur. At a later time, the recorded symptoms of the patient during the sensing event may be compared to the actual heart rhythm of the patient recorded by the apparatus 100, and this may help determine whether the patient's sensing event correlates to an actual cardiac event. However, one problem with patient event triggers in currently available wearable cardiac rhythm monitoring devices is that small triggers may be difficult to find and/or activate, particularly since the monitoring devices are typically worn under clothing. Additionally, pressing the trigger button may affect electronics and/or electrodes on the device in this manner: the recorded heart rate signal is then simply altered by the patient triggering a motion induced to the device. For example, pressing the trigger may vibrate one or both electrodes in this manner: the recorded cardiac rhythm signal appears to be an arrhythmia even though no actual arrhythmic event has occurred. In addition, there is an opportunity that the trigger may be inadvertently activated, for example while sleeping or lying on the monitoring device.

However, in the embodiment shown in fig. 1A and 1B, the rigid housing 115 is sufficiently rigid and the flexible body 110 is sufficiently flexible that movement imparted to the housing 115 by the patient causes little or permanently an abnormal signal to be sensed by the electrodes. In this embodiment, a central portion of the upper housing member 140 is slightly concave and, when depressed by a patient wearing the device 100, slightly concaves to trigger a trigger input on the PCBA 120. Since the entire upper surface of the rigid housing 115 acts as a patient event trigger, combined with the fact that it is slightly recessed, the patient can generally easily find and depress the trigger even under clothing. In addition, the concave nature of the button allows it to be recessed, which protects it from inadvertent activation. Thus, the present embodiments may alleviate some of the problems encountered with patient event triggers on currently available heart rate monitors. These and other aspects of the features shown in fig. 1A and 1B will be described in more detail below.

Referring now to the embodiment in fig. 2A and 2B, the printed circuit board assembly 120 (or PCBA) may include a top surface 220, a bottom surface 230, a patient trigger input 210, and spring contacts 235, 236, and 237. The printed circuit board assembly 120 may be used to mechanically support and electrically connect electronic components using conductive paths, tracks, or electrode traces 311, 312. Furthermore, due to the sensitivity of the PCBA120 and the need for mechanical engagement with the rigid body 115, it is beneficial to have the PCBA120 rigid enough to prevent undesirable deflection (deflections) that may introduce noise or artifacts into the ECG signal. This is particularly possible during patient-triggered activation when forces are transmitted through the rigid body 115 and into the PCBA 120. In some embodiments, one way to ensure the stiffness of the PCBA is to ensure that the thickness of the PCBA is relatively above a certain value. For example, a thickness of at least about 0.08cm is desirable, and more preferably, a thickness of at least about 0.17cm is desirable. In such applications, the PCBA120 may also be referred to as or replaced by a Printed Circuit Board (PCB), a Printed Wiring Board (PWB), an etched wiring board, or a Printed Circuit Assembly (PCA). In some embodiments, a wire wrap or point-to-point structure may be used in addition to PCBA120 or instead of PCBA 120. The PCBA120 may include analog circuitry and digital circuitry.

The patient trigger input 210 may be configured to transmit a signal from a patient trigger, such as the upper housing member 140 described above, to the PCBA 120. For example, the patient trigger input 210 may be a PCB switch or button responsive to pressure from a patient trigger (e.g., the upper surface of the upper housing portion 140). In various embodiments, the patient trigger input 210 may be a surface mount switch, a tactile switch, an LED illuminated tactile switch, or the like. In some embodiments, the patient trigger input 210 may also activate an indicator such as an LED. Some embodiments may involve triggers that are remotely located, such as on a separate device or as a smartphone application.

One significant challenge in collecting heart rhythm signals from a human or animal subject using a small two-electrode physiological monitoring device, such as device 100 described herein, is that only two electrodes can sometimes provide a limited viewing angle when attempting to distinguish between artifact and clinically significant signals. For example, when a left-handed patient brushes her teeth while wearing a small, dual-electrode physiological monitoring device on her left chest, brushing may often introduce motion artifacts that cause the recorded signal to appear very similar to ventricular tachycardia, a severe arrhythmia. Adding additional wires (and, therefore, carriers) is a traditional way to alleviate this concern, but this is typically done by adding extra wires adhered to the patient's chest at various locations, such as with a holter monitor. This approach is not compatible with small, wearable long-term monitors such as physiological monitoring device 100.

An alternative to the above problem is to provide one or more additional data channels to assist in signal discrimination. In some embodiments, for example, the apparatus 100 may include a data channel for detecting patch movement. In some embodiments, an accelerometer or other suitable device may provide patch motion by simply analyzing the amplitude variations of a single axis measurement or, alternatively, a combination of all three axes. The accelerometer can record device motion at a sufficient sampling rate to allow its spectrum to be arithmetically compared to the spectrum of the recorded ECG signal. If there is a match between the motion and the recorded signal, it is clear that the device recorded during this time period is not from a clinical (e.g. cardiac) source, and therefore parts of the signal can be confidently marked as artifacts. This technique may be particularly useful in the foregoing brushing motion examples, where the rapid motion frequency and high amplitude artifacts resemble the heart rate and morphology, respectively, of potentially life-threatening arrhythmias such as ventricular tachycardia. Other suitable devices described in this section and elsewhere in the specification herein may also be used to provide motion information.

In some embodiments, using the magnitudes of all three axes for such analysis will smooth out any sudden changes in value due to positional movement rather than activity changes. In other embodiments, there may be some advantages in using a particular measurement axis, such as along the longitudinal axis of the body, to focus on the particular type of artifact introduced by the upward and downward motion associated with walking or running. Similarly, the use of a gyroscope in conjunction with an accelerometer may provide a higher degree of resolution as to the nature of the motion experienced. While whole body motion may be adequately analyzed with the accelerometers themselves, the particular motion of interest, such as rotational motion due to arm motion, is sufficiently complex that the individual accelerometers may not be distinguishable.

In addition to detecting motion artifacts, accelerometers tuned to the dynamic range of human activity may also provide the patient's activity level during recording, which may also improve the accuracy of algorithmic true arrhythmia detection. In view of the single wire limitations of device 100, arrhythmias such as supraventricular tachycardia that require observation of less pronounced waves (e.g., P-waves) in addition to rate variations pose challenges to both computerized algorithms and to a trained human eye. This particular arrhythmia is also characterized by paroxysmal episodes that may be more confidently distinguished from non-pathological sinus tachycardia if a sudden increase in the patient's activity level is detected simultaneously with the increase in heart rate. Broadly speaking, providing activity information to a clinical professional may help them distinguish between motor arrhythmias and non-motor arrhythmias. As with motion artifact detection, a single axis accelerometer measurement optimized for a particular direction may be helpful in more specifically determining a type of activity such as walking or running. This additional information may help to more clearly interpret the symptoms, thereby affecting the course of subsequent therapeutic behavior.

In some embodiments, an accelerometer with 3 axes may give the advantage of providing more than what magnitude of motion. When the subject is not moving rapidly, the three-dimensional accelerometer readings may approximate the inclination of the PCBA120, and thus the body orientation, relative to its original orientation. The original body orientation may be assumed to be in the upright or supine position required to properly position and apply the device to the body. This information may help to exclude some cardiac conditions that manifest as beat-to-beat morphological changes, such as the cyclical amplitude-varying cardiac alternans typically observed in heart failure cases. Due to the movement of the heart position relative to the electrode carrier, e.g. from an upright position to a lazy position, similar beat-to-beat morphology changes are observed when the body position changes in healthy subjects. By design, the single channel device 100 does not have an optional ECG channel that easily excludes morphologically potential pathological changes, however, correlation to body orientation changes will help explain these normal changes and avoid unnecessary treatment due to misdiagnosis.

In other embodiments, an accelerometer may also be used as a sleep indicator based on body orientation and motion. When a clinical event (e.g., a pause) occurs, it is diagnostically helpful to be able to present the information in a manner that clearly separates events that occur during sleep from events that occur at wake time. In fact, some algorithms, such as ECG-derived respiration rates, only operate meaningfully when the patient is in a relatively stationary state, and thus subtle signal modulations due to respiration-induced chest motion can be observed. The respiration rate information is useful as an information channel necessary to detect sleep apnea in some patient populations.

In some embodiments, an accelerometer may also be used to detect free fall such as syncope. With an accelerometer, the device 100 may be able to flag syncope (syncope) and other free fall events without relying on a patient trigger. In some embodiments, such a free fall event trigger may initiate transmission of associated data. To detect these critical events in a timely manner, the acquisition of accelerometer readings may be performed in bursts where only information such as that of potential interest in free fall is written to memory at a high sampling rate, taking into account battery and memory limitations of small, wearable devices such as device 100. An extension of this event trigger concept is to use a specific tapping action on the device 100 as a patient trigger instead of or in conjunction with the previously described buttons. Using and detecting multiple types of tap sequences may provide better resolution and accuracy for the patient's accurate sense, rather than relying on the patient manually recording symptoms and duration in his trigger log after the fact. An example of such added resolution is to indicate the severity of the symptom by the number of sequential taps.

Alternatively, in other embodiments, an optical sensor may be used to distinguish between device motion and patient body motion. Further, in further embodiments, the device may not require a button or trigger. In further embodiments, suitable devices described in this section or elsewhere in the specification may also be used.

Another optional data channel that may be added to the physiological monitoring device 100 is a channel for detecting bending and/or buckling of the device 100. In various embodiments, for example, the apparatus 100 may include strain gauges, piezoelectric sensors, or optical sensors for detecting motion artifacts in the apparatus 100 itself, thereby facilitating the differentiation of motion artifacts from heart rhythm data. Another optional data channel of the apparatus 100 may be a channel for detecting heart rate. For example, a pulse oximeter, microphone, or stethoscope may provide heart rate information. Redundant heart rate data may help to distinguish ECG signals from artifacts. This is particularly useful in the case of cardiac arrhythmias such as supraventricular tachycardia which are interrupted by an artifact and a decision must be made whether the episode is actually a plurality of shorter episodes or a sustained episode. Another data channel for detecting ambient electrical noise may be included. For example, the apparatus 100 may comprise an antenna for picking up electromagnetic interference. Detecting electromagnetic interference may help to distinguish electrical noise from the actual ECG signal. Any of the above data channels may be stored to support future noise discrimination or applied in real time to immediately determine clinical effectiveness.

Referring now to the embodiment of fig. 3A and 3B, the flexible body 110 is shown in greater detail. As shown in fig. 3A, flexible body 110 can include wings 130, 131, a thin border 133 (or "rim" or "edge") around at least a portion of each wing 130, 131, electrode traces 311, 312, and a hinge 132 (or "shoulder") at or near the junction of each wing 130, 131 with rigid housing 115. Also shown in fig. 3A is an upper gasket 370, which is not considered part of the flexible body 110 for this description, but which facilitates attachment of the flexible body 110 to the rigid housing 115.

Hinge portion 132 is a relatively thinner, even more flexible portion of flexible body 110. They allow the flexible body 110 to freely bend at the area of connection with the rigid housing 115. This flexibility enhances comfort because the housing 115 may be free to move away from the patient's skin as the patient moves. The electrode traces 311, 312 are also very thin and flexible to allow the patient to move without signal distortion. The boundary 133 is a portion of the flexible body 110 that is thinner than immediately adjacent portions and provides a smooth transition from the flexible body 110 to the patient's skin, thereby preventing lifting and penetration of the edge of dust or debris beneath the flexible body 110.

As shown in more detail in fig. 3B, the flexible body 110 may include multiple layers. As previously mentioned, in some embodiments, for purposes of illustration, the upper and lower washers 370, 360 are not considered part of the flexible body 110, but are shown for completeness of illustration. However, such differences are merely for ease of description and should not be construed as limiting the scope of the embodiments. The flexible body 110 may include a top substrate layer 300, a bottom substrate layer 330, an adhesive layer 340, and a flexible electrode 350. The top substrate layer 300 and the bottom substrate layer 330 may be made of any suitable flexible material, such as one or more flexible polymers. Suitable flexible polymers may include, but are not limited to, polyurethane, polyethylene, polyester, polypropylene, nylon, polytetrafluoroethylene, and carbon impregnated vinyl. The materials of the substrate layers 300, 330 may be selected based on desired characteristics. For example, the material of the substrate layers 300, 330 may be selected for flexibility, elasticity, durability, breathability, moisture transpiration, adhesion, and/or the like. In one embodiment, for example, the top substrate layer 300 may be made of polyurethane and the bottom substrate layer 330 may be made of polyethylene or, alternatively, polyester. In other embodiments, the substrate layers 300, 330 may be made of the same material. In another embodiment, substrate layer 330 may comprise a plurality of perforations in areas on adhesive layer 340 that provide even more breathability and moisture vapor transmission. In various embodiments, the physiological monitor device 100 can be worn by the patient continuously for up to 14 days to 21 days or more without being removed during wear, and the device 100 is worn during showering, exercise, etc. Thus, the materials used and the thickness and configuration of the substrate layers 300, 330 affect the functionality of the physiological monitor device 100. In some embodiments, the material of the substrate layers 300, 330 serves as an electrostatic discharge (ESD) barrier to prevent arcing.

Typically, the top substrate layer 300 and the bottom substrate layer 330 are attached to each other by an adhesive disposed on one or both layers 300, 330. For example, the adhesive or bonding substance between the substrate layers 300, 330 may be an acrylic, rubber, or silicone based adhesive. In other alternative embodiments, the flexible body 110 may include more than two layers of flexible material.

In addition to selecting materials, the dimensions-thickness, length, and width-of substrate layers 300, 330 may be selected based on desired characteristics of flexible body 110. For example, in various embodiments, the thickness of substrate layers 300, 330 may be selected to provide a total thickness of flexible body 110 of between about 0.1mm to about 1.0 mm. According to various embodiments, the flexible body 110 may also have a length of between about 7cm and 15cm and a width of about 3cm and about 6 cm. Typically, the flexible body 110 will have a length sufficient to provide the necessary amount of spacing between the electrodes 350. For example, in one embodiment, the distance from the center of one electrode 350 to the center of the other electrode 350 should be at least about 6.0cm, more preferably at least about 8.5cm. The separation distance may vary depending on the application. In some embodiments, substrate layers 300, 330 may all have the same thickness. Alternatively, the two substrate layers 300, 330 may have different thicknesses.

As described above, hinge portion 132 allows rigid body 115 to be lifted off the patient while flexible body 110 remains adhered to the skin. The function of hinge portion 132 is critical to allow the device to remain adhered to the patient in a variety of activities that can stretch and compress the skin. Furthermore, the hinge portion 132 allows for a significant increase in comfort when wearing the device. Typically, the hinge portion 132 will be wide enough to provide adequate lifting of the rigid body 115 without creating too much peel force on the flexible body 110. For example, in various embodiments, the width of hinge portion 132 should be at least about 0.25cm, and more preferably at least about 0.75cm.

Additionally, the shape or footprint (footprint) of the flexible body 110 may be selected based on the desired characteristics. As shown in fig. 3A, the wings 130, 131 and the border 133 can have rounded edges that give the flexible body 110 an overall "peanut" shape. However, the wings 130, 131 may be formed in any number of different shapes, such as rectangular, oval, circular, or strip. In the embodiment shown in fig. 3A and 3B, the footprint of the top substrate layer 300 is larger than the footprint of the bottom substrate layer 330, wherein the extension of the top substrate layer 300 forms the boundary 133. Thus, the border 133 is made of the same polyurethane material that the top layer 300 is made of. Because the boundary 133 includes only the top layer 300, the boundary 133 is thinner than the adjacent portions of each wing 130, 131. Because the thinner, highly compatible edge 133 provides a transition from the adjacent, slightly thicker portions of the wings 130, 131 to the skin of the patient, it will likely enhance the compliance of the physiological monitoring device 100 to the patient, thus helping to prevent the edge of the device 110 from peeling away from the skin. The border 133 may also help prevent dirt and other debris from collecting under the flexible body 110, which helps promote adhesion to the skin and also improves the aesthetics of the device 110. In alternative embodiments, the footprint of the substrate layers 300, 330 may be the same, thereby eliminating the boundary 133.

Although the embodiment illustrated in fig. 1A-3B includes only two wings 130, 131 extending from rigid housing 115 in approximately opposite directions (e.g., at an angle of 180 degrees relative to each other), other configurations are possible in alternative embodiments. For example, in some embodiments, wings 130, 131 may be arranged in an asymmetric orientation relative to one another and/or may include one or more additional wings. Any suitable configuration and number of wings 130, 131 and electrode traces 311, 312 may be used, as long as sufficient electrode spacing is provided to allow physiological signal monitoring, and as long as the wings 130, 131 are configured to provide extended attachment to the skin. While the above embodiments have proven advantageous with respect to compliance with collected heart rhythm data, patient comfort, and accuracy, in alternative embodiments, alternative configurations are possible.

Adhesive layer 340 is an adhesive applied to two portions of the bottom surface of bottom substrate layer 330, each portion corresponding to one of wings 130, 131. Thus, adhesive layer 340 does not extend along the portion of bottom substrate layer 330 on which rigid housing 115 is mounted. While some adhesives have been found to be beneficial in providing a relatively comfortable and long-term adhesion to the skin of a patient without skin irritation, the adhesive layer 340 may be made of any suitable adhesive. For example, in one embodiment, the adhesive layer 340 is a hydrocolloid adhesive. In another embodiment, the adhesive layer 340 is comprised of a hydrocolloid adhesive containing naturally derived or synthetic absorbent materials that absorb moisture from the skin during sweating.

Referring now to fig. 3B, each of the two portions of the adhesive layer 340 includes an aperture that fits into one of the electrodes 350. The electrodes 350 are made of a flexible material that further provides the overall flexibility of the flexible body 110. In one embodiment, for example, the flexible electrode 350 may be made of hydrogel 350. The electrodes 350 generally provide conformal, non-irritating contact with the skin to provide enhanced electrical connection to the skin and reduce motion artifacts. In some embodiments, the hydrogel electrode 350 may be punched into the adhesive layer 340, forming a hole and filling the hole with the hydrogel electrode 350. In an alternative embodiment, the electrodes 350 and adhesive 340 may be replaced with an adhesive layer made of a conductive material such that the entire adhesive layer on the underside of each wing 130, 131 functions as an electrode. Such an adhesive layer may comprise a mixed adhesive/conductive substance or adhesive substance mixed with conductive elements or conductive particles. For example, in one embodiment, such an adhesive layer may be a mixture of a hydrogel and a hydrocolloid adhesive. The rigid housing 115 of fig. 1A also protects the electronic components and power sources contained in the housing 120, enhances the patient's ability to provide input related to sensed cardiac events, and allows for simple manufacture and reusability of at least some of the contents of the housing 115. These and other features of the physiological monitoring device 100 are described in more detail below.

As discussed above, in some embodiments, the adhesive layer 340 may cover portions of the underside of the lower substrate layer 330 such that at least a portion of the underside of the flexible body 110 does not include the adhesive layer 340. As shown in fig. 3A, hinge 132 may be formed in flexible body 110 as part of each wing 130, 131 where adhesive layer 340 is not applied. Hinge portion 132 is generally located at or near the junction of flexible body 110 and rigid housing 115, thus providing for flexing of device 100 to accommodate patient movement. In some embodiments, hinge portion 132 may have a width less than the adjacent portions of wings 130, 131, thereby giving device 100 its "peanut" shape as described above. As shown in fig. 8, when the subject moves, the device 100 flexes as the patient moves. Device buckling can be severe and can occur multiple times during long-term monitoring. Hinge portion 132 may allow dynamic compliance to the subject, while the rigidity of rigid housing 115 may allow housing 115 to pop out from the patient's skin during device flexion, thereby preventing device 100 from disengaging the skin at its edges.

The flexible body 110 further comprises two electrode traces 311, 312 sandwiched between the upper substrate layer 300 and the lower substrate layer 330. Each electrode trace 311, 312 may include an electrode joint 310 and an electrocardiographic circuit joint 313. As shown in the embodiments of fig. 3C and 3D, the ECG circuit interface 313 is in physical contact with the spring fingers 237 and provides electrical communication with the PCBA120 when the device 100 or the enlarged device portion 101 is assembled. The electrode interface 310 contacts the hydrogel electrode 350. Accordingly, the electrode traces 311, 312 transmit the heart rate signals (and/or other physiological data in various embodiments) from the electrodes 350 to the PCBA 120.

The material and thickness of the electrode traces 311, 312 are important to provide the desired combination of flexibility, durability, and signal transmission. For example, in one embodiment, the electrode traces 311, 312 may include a combination of silver (Ag) and silver chloride (AgCl). The silver and silver chloride may be layered. For example, one embodiment of the electrode traces 311, 312 may include a top layer of silver, a middle layer of carbon-impregnated vinyl, and a bottom (patient-facing) layer of silver chloride. In another embodiment, both the top and bottom layers of the electrode traces 311, 312 may be made of silver chloride. In one embodiment, the top and bottom layers may be applied to the middle layer in the form of silver ink and silver chloride ink, respectively. In alternative embodiments, each electrode trace may include only two layers such as a top layer of silver and a bottom layer of silver chloride. In various embodiments, the material of the bottom layer of each electrode trace 311, 312, such as AgCl, may be selected to match the chemistry of the hydrogel electrode 350 and create a half-cell with the subject's body.

The thickness of the electrode traces 311, 312 may be selected to optimize any of a number of desired properties. For example, in some embodiments, at least one of the layers of electrode traces 311, 312 may have a sufficient thickness to minimize or slow the depletion of the anode/cathode effect of the material over time. Additionally, the thickness may be selected for desired flexibility, durability, and/or signal transmission quality.

As described above, in some embodiments, the top gasket 370 and the bottom gasket 360 may be attached to the upper substrate 300 and the lower substrate 330 of the flexible body 110. The gaskets 360, 370 may be made of any suitable material that provides a waterproof seal between the upper and lower housing members 140, 145 of the rigid housing 115, such as polyurethane. In one embodiment, the top gasket 370 and/or the bottom gasket 360 may include an adhesive surface. Fig. 3E depicts another embodiment in which the top gasket 370 includes tabs 371 that protrude from the outline of the top housing 140 while still adhering to the upper substrate 300. The tabs 371 cover portions of the electrode traces 311, 312 and provide strain relief for the traces at the highest stress points where the flexible body meets the rigid housing.

Referring now to the embodiment of FIG. 4, the upper and lower housing members 140, 145 of the rigid housing 115 are shown in greater detail. When coupled together with the gaskets 360, 370 between the upper and lower housing members 140, 145, the upper and lower housing members 140, 145 may be configured to form a waterproof enclosure for housing the PCBA120, the battery holder 150, the battery 160, and any other components contained within the rigid housing 115. The housing members 140, 145 may be made of any suitable material that protects the internal components, such as waterproof plastic. In one embodiment, the upper housing member 140 may include rigid sidewalls 440, a light pipe 410 for transmitting visual information from the LEDs on the PCBA through the housing member, a slightly flexible top surface 420, and an internal trigger member 430 extending inwardly from the top surface 420. The top surface 420 is configured to be depressed by the patient when the patient perceives that he or she is considering an arrhythmia or other cardiac event. When depressed, the top surface 420 depresses an inner trigger member 430 that contacts and activates the trigger input 210 of the PCBA 120. Additionally, as previously described, the top surface 420 may have a concave shape (concave surface facing the interior of the housing 115) to accommodate the shape of a finger. It is believed that upper housing member 140 is designed to isolate activation of trigger input 210 from electrode 350, thereby minimizing artifacts in data recording.

With continued reference to fig. 4, lower housing member 145 may be configured to be removably connected with upper housing member 140 in a manner such that housing members 140, 145 may be easily attached and detached for reusability of at least some components of monitoring device 100. In some embodiments, the bottom surface 445 (patient-facing surface) of the lower housing member 145 can include a plurality of protrusions 450 (or "bumps," "protrusions," etc.) that will contact the patient's skin during use. The protrusions 450 may allow air flow between the bottom surface 445 and the patient's skin, thereby preventing a seal from being formed between the bottom surface 445 and the skin. It is believed that the projections 450 improve comfort and help prevent feelings in currently available devices, where the patient feels as if the monitoring device 100 falls off when its housing 115 is lifted from the skin and breaks the seal with the skin. In another embodiment, the bottom surface 445 of the lower housing member 450 may include a plurality of depressions (recesses rather than protrusions) that prevent the formation of a seal.

Referring now to the embodiment of fig. 5A, the battery holder 150 is shown in more detail. Battery holder 150, which may be made of plastic or other suitable material, is configured to mount to PCBA120 and then attach to rigid housing 115 and is capable of holding two batteries 160 (fig. 1B). In alternative embodiments, the battery holder 150 may be configured to hold one battery or more than two batteries. The plurality of protrusions 152 provide a stable platform for the battery 160 to be positioned at a fixed distance above the surface of the PCBA120, thereby avoiding undesired contact with sensitive electronic components, yet providing sufficient compression of the spring contacts 235 (fig. 5B). The tabs 153 lock the battery 160 in place and resist the upward force from the spring contacts 235 against the battery. The battery holder 150 also properly positions the battery 160 to provide sufficient compression of the spring contacts 236. The use of battery holder 150 in conjunction with spring contacts 235 and 236 allows battery 160 to be electrically connected to PCBA120 while still having additional electronic components between battery 160 and PCBA120 and maintaining a very compact assembly. The battery holder 150 may include flexible hooks 510 that engage with corresponding rigid hooks 440 of the upper housing member 140. Under normal assembly conditions, flexible hook 510 remains securely engaged with rigid hook 440. For disassembly, a suitable tool through top case 140 may be used to push and bend flexible hook 510 so that it disengages from rigid hook 440 and then allows top case 140 to be removed.

Referring now to the embodiment of fig. 6A and 6B, the physiological monitoring device 100 is shown in cross-section in a side view. As shown in fig. 6A, the physiological monitoring device 100 can include a flexible body 110 coupled with a rigid housing 115. The flexible body 110 may include a top substrate layer 300, a bottom substrate layer 330, an adhesive layer 340, and an electrode 350. The electrode traces 311, 312 are also typically part of the flexible body 110 and are embedded between the top substrate layer 300 and the bottom substrate layer 330, but they are not shown in fig. 6. Flexible body 110 forms two wings 130, 131 extending to either side of housing 115 and a border 133 surrounding at least a portion of each wing 130, 131. Rigid housing 115 may include an upper housing member 140 coupled with a lower housing member 145 such that it sandwiches portions of flexible body 110 therebetween and provides a waterproof sealed compartment for PCBA 120. The upper housing member 140 may include an internal trigger member 430, and the PCBA may include the patient trigger member 210. As previously discussed, lower housing member 145 may include a plurality of projections 450 or recesses that enhance the comfort of monitoring device 100.

It is desirable for the PCBA120 to be sufficiently rigid to prevent bending and to prevent introducing undesirable artifacts into the signal. In some embodiments, additional mechanisms for reducing and preventing undesired bending of the PCBA120 can be used. Such a mechanism is shown in fig. 6B. The support column 460 is integral with the lower housing 145 and is positioned directly below the patient trigger input 210. During patient symptom triggering, the upper housing member 140 is depressed, engaging the internal trigger mechanism 430 and transmitting the force through the patient trigger input 210 into the PCBA 120. This force is further transmitted through the PCBA120 into the support posts 460 without creating a bending moment, thereby avoiding undesirable artifacts.

Referring to fig. 7, in some embodiments, the physiological monitoring device 100 can include one or more additional optional features. For example, in one embodiment, the monitoring device 100 may include a removable liner 810, a top label 820, a device identifier 830, and a bottom label 840. A pad 810 may be applied on the top surface of the flexible member 110 to aid in applying the device 100 to a subject. As described in further detail below, the pad 810 may help support the border 133 and wings 130, 131 of the flexible body 110 when one or more adhesive covers (not shown) covering the adhesive surface 340 are removed prior to use. The pad 810 may be relatively rigid and/or strong to help support the flexible body 110 during removal of the adhesive cover. In various embodiments, for example, the liner 810 may be made of cardboard, thick paper, plastic, and the like. The pad 810 generally includes an adhesive on one side for adhering to the top surface of the wings 130, 131 of the flexible body 110.

The tags 820, 840 may be any suitable tag and may include a product name, a manufacturer name, a logo, a design, and/or the like. While typically the tags will be permanently attached to avoid unregulated reuse and/or resale of the device by unregistered users, they may be removable or permanently attached to the upper and/or lower housing members 140, 145. The device identifier 830 may be a bar code label, a computer readable chip, an RFID, etc. Device identifier 830 may be permanently or removably attached to PCBA120, flexible body 110, etc. In some embodiments, it may be beneficial to have device identifier 830 adhere to PCBA 120.

Referring now to the embodiment of fig. 8A and 8B, the physiological monitoring device 100 generally includes a hinge portion 132 at or near the junction of each wing 130, 131 and the rigid housing 115. In addition, each wing 130, 131 is generally adhered to the patient by the adhesive layer 340, while the rigid body 115 is not adhered to the patient and is therefore free to "float" (e.g., move up and down) on the patient's skin during movement and changes in patient position. In other words, when the patient's chest contracts, the rigid shell springs or floats over the skin, thereby minimizing stress on the device 100, enhancing comfort, and reducing the tendency of the wings 130, 131 to peel away from the skin. Fig. 8A and 8B illustrate the advantages provided by the combination of floating rigid body 115 and adhered wings 130, 131. In fig. 8A, the patient is sleeping and in fig. 8B, the patient is playing golf. In both examples, the monitoring device 100 is squeezed together by the patient's body such that the rigid housing 115 floats above the skin when the wings 130, 131 are closer to each other. This advantage of the floating, non-attached portion of the physiological monitoring device is described in further detail in U.S. patent No. 8,560,046, previously incorporated herein by reference.

Referring now to fig. 9A-9F, one embodiment of a method of applying the physiological monitor device 100 to the skin of a human subject is described. In this embodiment, prior to the first step shown in fig. 9A, the patient's skin may be generally prepared by shaving a small portion of the skin of the left chest where the device 100 is to be placed and then scraping and/or cleaning the shaved portion. Once the patient's skin is properly prepared, a first step of applying the device 100 may include removing one or both of the two adhesive covers 600 from the adhesive layer 340 on the bottom surface of the device 100, thereby exposing the adhesive layer 340, as shown in fig. 9A. As shown in fig. 9B, the next step may be to apply the device 100 to the skin such that the adhesive layer 340 adheres to the skin at the desired location. In some embodiments, one adhesive cover 600 may be removed, the uncovered adhesive layer 340 may be applied to the skin, then the second adhesive cover 600 may be removed, and the second adhesive layer 340 may be applied to the skin. Optionally, both adhesive covers 600 may be removed prior to applying the device 100 to the skin. When the adhesive cover 600 is removed, the pad 810 serves as a support for the flexible body 110, provides a physician or other user with something to hold, and prevents the flexible body 110 and the border 133 of the flexible body 110 from folding over on itself, forming wrinkles, etc. As described above, the pad 810 may be made of a relatively rigid, sturdy material to provide support to the flexible body 110 when the device 100 is applied to the skin. Referring to fig. 9C, after the device 100 has been applied to the skin, pressure may be applied to the flexible body 110 to depress it onto the chest to help ensure adhesion of the device 100 to the skin.

In a next step, referring to fig. 9D, the liner 810 is removed (e.g., peeled) from the top surface of the flexible body 110. Once the pad 810 is removed, pressure may again be applied to the flexible body 110 to help ensure its adherence to the skin, as shown in fig. 9E. Finally, as shown in fig. 9F, the upper housing member 140 can be pressed to open the physiological monitor device 140. The method described is only one embodiment. In alternative embodiments, one or more steps may be skipped and/or one or more additional steps may be added.

In some embodiments, when the desired monitoring period has ended, such as about 14 to 21 days in some cases, the patient (or physician, nurse, etc.) can remove the physiological monitoring device 100 from the patient's skin, place the device 100 in a pre-paid mailer, and mail the device 100 to the data processing apparatus. In this arrangement, the apparatus 100 may be partially or fully disassembled, the PCBA120 may be removed, and stored physiological data such as continuous heart rate information may be downloaded from the apparatus 100. The data may then be analyzed by any suitable method and then provided to the physician in the form of a report. The doctor can then discuss the report with the patient. PCBA120 and/or other portions of device 100, such as rigid housing 115, can be reused in the manufacture of subsequent devices for the same or other patients. Because apparatus 100 is constructed as a combination of several removably coupled components, each component can be reused with the same embodiment or a different embodiment of apparatus 100. For example, the PCBA120 may be used first in an adult heart rhythm monitor, and then may be used a second time to construct a monitor for sleep apnea. The same PCBA120 can additionally or alternatively be used with different sized flexible bodies 110 to construct a pediatric heart monitor. Accordingly, at least some of the component parts of the device 100 may be interchangeable and reusable.

In further embodiments described in more detail below, the monitoring data may be transmitted for analysis wirelessly or through other communication media without requiring physical shipment of the device for analysis and reporting.

Advantageously, the physiological monitor device 100 can provide long-term adhesion to the skin. The combination of the configuration of the flexible and conformal body 110, the water-tightness of the rigid housing 115, the low-profile configuration, and the interface therebetween allows the device 100 to compensate for stresses induced when the skin of a subject is stretched and bent. As a result, the device 100 can be continuously worn on the patient for up to 14 to 21 days or more without removal. In some cases, while device 100 may be worn for longer or shorter periods of time, 14 to 21 days may generally be a desired amount of time for collecting cardiac rhythm data and/or other physiological signal data from a patient.

In various alternative embodiments, the shape of a particular physiological monitoring device may vary. For example, the shape, footprint, perimeter, or boundary of the device may be, for example, circular, elliptical, triangular, compound curved, or the like. In some embodiments, the compound curve may include one or more concave curves and one or more convex curves. The convex shapes may be separated by concave portions. The recess may be between a protrusion on the rigid housing and a protrusion on the electrode. In some embodiments, the recess may correspond at least in part to a hinge, a hinge region, or a region of reduced thickness between the body and the wing.

Although described in the context of a cardiac monitor, the device improvements described herein are not so limited. The improvements described in this application may be applied to any of a variety of physiological data monitoring, recording and/or transmitting devices. The improved adhesion design features may also be applied to devices useful in electronically controlled and/or timed release delivery of drugs or blood testing, such as glucose monitors or other blood testing devices. Thus, the descriptions, characteristics, and functions of the components described herein may be modified as necessary to include specific components of a particular application, such as the following: electronics, antennas, power or charging connections, data ports or connections for downloading or unloading information from the device, adding or unloading fluids from the device, monitoring or sensing elements such as electrodes, probes or sensors, or any other component or part required in the specific function of the device. Additionally or alternatively, devices described herein including, but not limited to, one or more of ECG, EEG, and/or EMG may be used to detect, record, or transmit signals or information related to signals produced by the body. In some embodiments, additional data channels may be included that collect additional data such as device motion, device bending or flexing, heart rate, and/or ambient electrical or acoustic noise.

The physiological monitor described above and elsewhere in this specification can also be combined with methods and systems that improve data processing and transmission of data acquisition from the monitor. Further, the methods and systems described below may improve the performance of the monitor by enabling timely transmission of clinical information while maintaining a high degree of patient compliance and ease of use of the monitor described above. For example, the methods and systems of data processing and transmission described in this and other portions of this specification may be used to extend the battery life of the monitor, improve the accuracy of the monitor, and/or provide other improvements and advantages described in this or other portions of this specification.

Device monitoring and clinical analysis platform

The systems and methods described in detail below with reference to the embodiments of fig. 10-17 may selectively extract, transmit, and analyze electrocardiographic signal data and other physiological data from a wearable physiological monitor, such as the wearable physiological monitor described above with respect to fig. 1-9. The systems and methods described below may improve the performance of a wearable physiological monitor that records and transmits data via multiple devices simultaneously. For example, the selective transmission of extracted data allows for reduced power consumption, since the wearable patch is not required to transmit all recorded data. By transmitting the extracted data, a large number of analyses can be performed remotely from the wearable device without the need for a complete on-board rhythm analysis, which can also be high power consuming, thereby reducing battery life. Further, remote analysis without the power limitations inherent in wearable devices may provide greater sensitivity and accuracy in analyzing data. Reducing power consumption may improve patient compliance because it extends the period of the monitoring cycle or even eliminates the need for device replacement, battery replacement, or battery recharging during the monitoring cycle. By reducing battery consumption, longer monitoring times can be achieved without requiring device replacement, e.g., at least one week, at least two weeks, at least three weeks, or more than three weeks.

Fig. 10 depicts a general overview of an embodiment of a system 900 for inferring heart rate information from an R-R interval time series 902 that may be produced by a continuous heart rate monitoring device 904. Such a system will be described in more detail below with reference to fig. 11-17. The R-R interval time series 902 input to the system may comprise a series of measurements of the timing interval between successive heartbeats. Typically, each interval represents a time period between two consecutive R-peaks identified from the ECG signal. The R peak is a portion of the QRS complex (complete), a combination of three pattern shifts typically seen on an ECG, representing the depolarization of the left and right ventricles of a mammalian heart. The R peak is usually the highest and most visible upward shift on the ECG, thus forming a suitable reference point. However, in further embodiments, any characteristic ECG fiducial point (such as the start or end of a QRS complex) may be used in place of the R peak to provide an estimate of the R-R interval time series. As described above with respect to fig. 1-9, the physical characteristics of the monitoring device are constructed in a manner that improves signal fidelity, and thus high signal fidelity allows for a high level of confidence in the R-R peak data to be accurately extracted.

The R-R interval time series 902 data may be extracted or received from a dedicated heart rate monitor such as: heart rate chest bands or rate tables, or wearable health or fitness devices 906, 908 that include heart rate sensing functionality. Optionally, R-R interval time series 902 may be derived from a wearable patch designed to measure ECG signal 904 (e.g., by locating the R peak in the ECG using a QRS detection algorithm). Further, the R-R interval time series 902 may be estimated from an alternative physiological signal such as a signal obtained from photoplethysmography (PPG). In this case, the peak-to-peak interval time series determined from the PPG signal can be used as an accurate estimate of the R-R interval time series.

In one aspect, rhythm inference system 910 is implemented as a cloud service or server-based system that exposes an Application Programming Interface (API) that enables R-R interval time series data or other signal data to be transmitted to the system (e.g., over HTTP) and returns the obtained rhythm information to the calling software. The R-R interval time series data 902 or other signal data may be transmitted to the cloud service directly from the heart rate monitoring device itself or indirectly through a smartphone 912, tablet, or other internet-enabled communication device 914 capable of receiving data from the heart rate monitoring device in a wireless or wired manner. In addition, R-R interval time series data 902 or other signals may be transmitted from a server 916 that stores data for multiple users.

In some embodiments, the rhythm inference system 910 is provided by a software library that may be incorporated into a stand-alone application for installation and use on a smartphone, tablet, or personal computer. The library may provide the same functionality as the inference service, but the R-R interval time series data 902 or other signal data is transmitted directly through the function call rather than through the web service API.

In some embodiments, the heart rhythm inference system may accept multiple R-R interval time series measured from a given user 918 device in addition to a single R-R interval time series 902. In this case, the system calculates the frequency and duration of each heart rhythm type inferred from the collection of time-series data. These results can then be used to estimate confidence statistics for each rhythm based on the frequency and duration of occurrence of that rhythm across various time series. Further, the rhythm confidence statistics may be updated in a sequential manner per each individual call of the inference service. Further, in some embodiments, rhythm information inferred by the system can only be provided back to the calling software if the confidence score for a given rhythm type exceeds a predetermined threshold.

In particular embodiments, heart rhythm inference system 910 may accept additional data sources, in addition to R-R interval time series data, often described as optional sensor channels to improve the accuracy and/or value of the inference results. One additional data source includes user activity time series data, such as data measured by a 3-axis accelerometer in parallel with the R-R interval time series measurements. In addition, the system may accept other relevant metadata that may help improve the accuracy of the rhythm analysis, such as the user's age, gender, monitoring instructions, pre-existing medical conditions, medication information, medical history, etc., as well as information about the specific date and time range of each time series submitted to the system. Furthermore, the measurement device may also provide some measure of beat detection confidence, e.g., for each R-peak or successive time periods. This confidence measure will be based on an analysis of the recorded signal that will not be recorded due to storage space and battery energy requirements in typical embodiments. Finally, in the particular case where R-R interval time series data is derived from the ECG signal, the system may accept additional signal features computed from the ECG. These features may include a time series of intra-beat (intra-beat) interval measurements, such as QT or PR intervals or QRS duration, or a time series of signal statistics, such as the average, median, standard deviation, or sum of ECG signal sample values over a given time period.

The various aspects described above may be used alone or in combination to provide applications that provide insight into the health, stress, sleep, fitness, and/or other qualities of an individual.

Some embodiments relate to methods and systems for selectively transmitting electrocardiogram signal data from wearable medical sensorsThe system is also disclosed. Battery charged once, current wearable sensors such as iRhythm Zio Patch ^TM And the wearable sensor described further above with reference to fig. 1-9 is capable of recording single lead Electrocardiogram (ECG) signals for up to two weeks. However, in many cases, it is desirable for the sensor to be able to transmit specific portions of the recorded ECG signal of clinical significance in real time or near real time to a computer device such as a smartphone 912 or an internet-connected gateway device 914 for subsequent processing and analysis. In this way, potentially valuable diagnostic ECG information may be provided to the patient or to the physician thereof during periods when the patient is wearing the sensors.

As noted above, a significant challenge with this approach is managing the battery life of the wearable sensors without requiring replacement or recharging, both of which reduce user compliance. Each transmission of ECG from the sensor to the smartphone or local gateway device (e.g., using bluetooth low energy) results in a subsequent reduction in the total charge stored in the sensor battery. Some embodiments of the present disclosure, particularly those of fig. 10-17, address this issue by using novel hardware and software combinations to enable selective transmission of clinically relevant portions of the ECG from wearable sensors.

In some embodiments, the wearable sensors include software, hardware, or hybrid QRS detectors that produce real-time estimates of the position of each R-peak in the ECG. The R peak position data is then used to calculate an R-R interval time series that is subsequently transmitted to the smartphone or gateway device according to a predetermined schedule (e.g., once per hour). In addition, a timestamp storing the start time of the R-R interval time series relative to the start of the ECG recording is also transmitted. Since the R-R interval time series for a given portion of the ECG is significantly smaller (in terms of the number of bytes occupied) than the ECG signal itself, it can be transmitted with a relatively small impact on battery life.

In some embodiments of the second stage of the system, the R-R interval time sequence along with the start time stamp is subsequently transmitted to the server by the smartphone or gateway device. On the server, the R-R interval time series is used to infer a list of most likely heart rhythms and their start and end times during the period represented by the time series data. The list of inferred heart rhythms is then filtered according to specific criteria so that only rhythms matching the given criteria remain after filtering. Confidence measures may also be used to filter events in a way that may improve the positive predictability of detection.

In some embodiments of the third stage of the system, for each rhythm in the filtered set of rhythms, the server transmits a start time and an end time for that particular rhythm to the smartphone or gateway device. In the event that the inferred rhythm duration exceeds a predetermined maximum duration, the start and end times may be adjusted such that the resulting duration is less than the maximum allowable duration. The start time and end time received by the gateway are then subsequently transmitted to the wearable sensor, which in turn transmits portions of the recorded ECG signal back to the gateway between the start time and the end time. The portion of the ECG is then transmitted to a server that can analyze the portion of the ECG and provide diagnostic information to the patient or his physician.

In some embodiments, the system fundamentally allows the device to be worn for up to about: 14 days, 21 days, or 30 days or more without battery recharging or replacement (two activities that reduce patient compliance and therefore diagnostic value) to provide timely communication of asymptomatic arrhythmic events. This development has been motivated by technical limitations: in order to achieve a small wearable device that does not require battery replacement or recharging while providing continuous arrhythmia analysis with high accuracy, it is desirable to limit the complexity of the analysis performed on-board. Similarly, streaming all recorded ECG data to an off-board analysis algorithm may not be practical without imposing greater power requirements. This motivates a more inventive "classification" approach in which selected features of the recorded ECG signal, including but not limited to R-R intervals, are transmitted for each beat, allowing custom algorithm positioning to request the number of 90 second events (e.g., 10) from the device at full resolution to support comprehensive analysis, e.g., resolution capable of supporting clinical diagnosis.

In other embodiments, the system will provide the ability to detect asymptomatic arrhythmias in a timely manner on a wearable adhesive-mounted device that does not require frequent recharging or replacement. This would serve to increase the value of some current clinical services that provide clinical observations only after the records are completed and returned for analysis.

In some embodiments, the system will allow viable clinical observations to be derived from data collected on low cost, easy to use consumer wearable devices that are otherwise dedicated only to fitness and health. For example, the techniques may be used to create a very efficient, low-cost screening tool that is capable of detecting the presence of atrial fibrillation in a large population. By using such a tool, not only can patients requiring care be more easily found, but it can be done earlier and more economically, which leads to better results-i.e. a reduced risk of stroke by identifying AF more quickly.

In particular embodiments, the system may provide a service through a downloadable application that, upon accepting customer approval for data access and payment approval, will initiate access and analysis of heartbeat data stored by the wearable device, either in the local mobile device or in an online repository. This data extraction (pull) and analysis will occur through the algorithmic API and will produce a clinical finding that is sent back to the application provided to the user. If the data is sufficient to support a "screening-oriented" discovery, e.g., "irregular rhythms may be detected", the application directs them to a service that may provide a more diagnostic focus, e.g.

A cardiologist who serves to support clinical diagnosis and treatment. In further embodiments, as described elsewhere in this specification, the system may trigger an alarm if a particular measurement and/or analysis indicates that an alarm is required.

Further examples of additional cases of clinical value may include linking a blood-alcohol monitor with dynamic arrhythmia monitoring to study the interaction of AF and lifestyle factors. For example, dynamic arrhythmia monitoring may be combined with a blood glucose monitor to study the effect of hypoglycemia on arrhythmia. Optionally, dynamic arrhythmia monitoring may be combined with respiration rate and/or respiration volume monitors to study the interaction of sleep apnea with respiratory disorders. Further, a high rate of ectopic beats on the chamber can be evaluated as potential precursors to AF (e.g., 720 SVEs in 24 hours).

Extraction, transmission and processing system

Fig. 11 is a schematic diagram of an embodiment of a system and method 1000 for a wearable medical sensor with transmission capability 1002 similar to the system and/or method described above with respect to fig. 10. In some embodiments, the sensor 1002, which may be any type of sensor or monitor described in this section herein or elsewhere in this specification, continuously senses the ECG or comparable biological signal 1004 and continuously records the ECG or comparable biological signal 1004. In some embodiments, the sensing step and/or the recording step may be performed intermittently. The collected signal 1004 may then be successively extracted into one or more features 1006 representing example features a, B, and C. These features are not intended to sample different temporal portions of the signal, but rather (as will be described in more detail below), different features may correspond to different types or different segments of data, such as R-peak position or R-peak amplitude. Features of the ECG or comparable biological signals are extracted to facilitate remote analysis of the signal 1004. In some embodiments, features are extracted on a window basis, where the window size varies, for example, between 1 or more hours to several seconds. In some embodiments, the window may be at most: about.1 second, about 1 second, about 2 seconds, about 3 seconds, about 5 seconds, about 10 seconds, about 30 seconds, about 1 minute, about 5 minutes, about 30 minutes, about 1 hour, about 2 hours, about 4 hours, or more than 4 hours. If repeated, the extraction windows may be separated by various amounts of time. For example, the extraction window may be at least: about 30 seconds, about 1 minute, about 5 minutes, about 30 minutes, about 1 hour, about 3 hours, about 6 hours, about 12 hours, about 24 hours, about 48 hours, or more than 3 days apart. In some embodiments, the window size may vary depending on the features extracted. Feature extraction may be limited to one type or various types of features, and the features selected for extraction may vary depending on the nature of the observed signal.

Various different types of ECG or comparable bio-signal features may be extracted. For example, the R peak position may be extracted. In some embodiments, the R peak position is extracted by various methods such as: pan-Tompkins algorithm (Pan-Tompkins, 1985), i.e. a real-time QRS complex detection algorithm using a series of digital filtering steps and adaptive thresholds, or an analog R peak detection circuit comprising an R peak detector consisting of a band-pass filter, a comparator circuit and a dynamic gain adjuster for locating the R peak, is provided. The RR interval can be calculated from the peak position and used as the main feature for rhythm discrimination. In an embodiment, an R peak overflow flag may be extracted. The flag may be caused by firmware if more than a certain number of R-peaks are detected during a given time window such that all data cannot be transmitted. Such extraction can be used to eliminate noise segments from analysis based on the extremely short intervals of R-R that are physiologically impossible. Assuming appropriate asystole considerations are made in this evaluation, under similar motivations, an R-peak underflow flag may be extracted to indicate an impractically long interval between successive R-peaks. In an alternative embodiment with the same goal, the absence of an R peak in an extended interval may be associated with a confidence measure that will describe the likelihood that the interval is clinical or artifact.

Another example of a feature 1006 that may be extracted includes a saturation flag for signal saturation during a given time window (e.g., 1 second), an indication of a firmware or hardware determination. This extraction can be used to eliminate noise segments from the analysis. In some embodiments, P/T wave positions may be extracted. This is similar to R-peak detection but tuned to lower frequency waves. The R peak position can be used to determine the area of possible wave components. Another example of a feature that may be extracted includes respiration rate. ECG-derived respiration (EDR) can be derived from studying the amplitude modulation of the ECG signal amplitude. EDR can be correlated with other clinical indicators of arrhythmia. In an embodiment, the R peak amplitude may be extracted by measuring the ECG signal amplitude at the R peak position. This pattern can be studied to distinguish between true and false peak detection and/or to detect changes in beat morphology.

In particular embodiments, an ECG signal amplitude proxy (proxy) may be extracted. The features may include: a range of raw signal data during a given time period, a maximum value of a signal during a given time period, or a minimum value of a signal during a given time period. This feature can be used as a data point for noise detection or possible changes in ECG morphology (e.g. ventricular ectopy). In some embodiments, additional ECG signal samples may be extracted. Sampling several data points at regular intervals or continuously from the region between selected R-peaks will allow the confidence of the rhythm and/or noise classification to be determined. This selection may be based on the length of the R-R interval. For example, if the interval is longer than 3 seconds, it may be an indication of a pause. For example, local ECG signal energy may also be extracted by taking the sum of the squares of the signal values within a window centered at a point of interest, such as an R-peak, to provide an integral of the ECG sample values over a given time window. This information can be used to characterize the morphology of the heartbeat (supraventricular tachycardia (SVT) versus Ventricular Tachycardia (VT)).

In some embodiments, the spectral information may be extracted by extracting statistical information from the output of one or more filters, which is implemented on hardware (during signal acquisition) or in firmware. The filters may be implemented as a bank of filters, such as a Short Time Fourier Transform (STFT) or wavelet transform. This information can be used to characterize the morphology of the heartbeat. The output of the simple machine learning model may be extracted. For example, given any combination of raw collected data values or features derived from available data channels, the likelihood of an ECG signal segment selected under a probabilistic model such as gaussian can be extracted. Using a simple machine learning model may allow less data to be transmitted. In an embodiment, the output may provide an understanding, directly or indirectly, of: the type of underlying rhythm, the presence of ECG features such as P-waves, the confidence level of R-peak detection, and the presence of noise.

Once the feature extraction is complete, as described above, the individual features 1008 may be transmitted 1010 to a processing device/server 1012. Features 1008 (as well as spare sensor channel data and/or features described below) are transmitted at regular intervals to processor 1012, which is not a physical part of sensor 1002. The interval definition may be preset, or configurable per use or dynamically configurable. The transmission 1010 of the feature 1008 may also be bundled and sent when there is another cause of communication, such as transmission of symptomatic data (described in more detail below with respect to fig. 16). In some embodiments, the processing device 1012 may be: a cloud-based server, a physical server at a corporate location, a physical server at a patient or clinic location, a smart phone, a tablet, a personal computer, a smart watch, an automobile console, an audio device, and/or a live or off-site standby device. In particular embodiments, transmission 1010 may utilize a short-range RF communication protocol such as: bluetooth, zigBee, wiFi (802.11), wireless USB, ANT or ANT +, ultra Wideband (UWB), and/or custom protocols. The transmission 1010 may be via infrared communication such as IrDA and/or inductive coupling communication such as NFC. In some embodiments, the transmission may be accomplished through a cellular data network and/or wired communication protocol such as USB, serial, TDMA, or other suitable custom device.

In some embodiments, the transmitted features 1014 are processed by a remote processor utilizing the data features 1014 to perform analysis via a rhythm inference system 1016 that analyzes and identifies segments/locations 1018 that may include arrhythmias. For example, arrhythmia and ectopic types that may be identified may include: pauses, second or third degree AVBs, complete heart block, SVT, AF, VT, VF, bigeminy, trigeminy, and/or ectopy. The determined confidence level may be included in the identification of the rhythm. Further, rhythm inference system 1016 may also utilize demographic data such as age, gender, or indication of the patient to improve accuracy and/or improve confidence of the determination.

The identified arrhythmia location 1018 is then transmitted 1020 back to the sensor 1002. The transmission 1020 back to the sensor may be accomplished via any communication protocol/technique described in this section herein or elsewhere in this specification, such as via bluetooth. The sensor then reads the transmitted identified location 1022 and accesses 1024 a memory area corresponding to the transmitted identified location 1022 of the ECG. In some embodiments, the sensor applies additional analysis of the identified segments to further establish a confidence in the arrhythmia identification. This further rhythm confidence determination step 1026 allows for increased positive predictability prior to the power-depleting transmission step. In an embodiment, if the confidence exceeds a defined threshold, the data segment is transmitted. For example, the defined threshold may be a preset value, or it may be set for each user and monitoring phase. In embodiments, the defined threshold may be dynamically changed depending on the nature of the rhythm, the history of accurate detection over the monitoring period, and/or the confidence of the rhythm inference system. Additional analysis may also be performed. Examples of possible analytical techniques include any of the methods disclosed herein in this section or elsewhere in this specification, for example: r peak amplitude, ECG signal amplitude proxy, ECG signal sampling, local ECG signal energy, spectral information, and/or output from a simple machine learning model.

If the confidence level exceeds the threshold as described above, the sensor 1002 can transmit the requested ECG segment 1028 to the processing device via any of the transmission means described in this section or elsewhere in this specification. The processing device may perform further analysis of the segments to confirm the accuracy of the predicted arrhythmia before reporting to the user and/or physician using the data, as desired.

Fig. 12 is a schematic diagram of an embodiment of a system and method 2000 with wearable ECG and/or medical sensors 2002 with transmission capability very similar to the system and/or method 1000 described above with reference to fig. 11. The system of fig. 12 differs from the system of fig. 11 in that the system of fig. 12 includes a secondary transmission 2004. For example, possible secondary transmission means include: smart phones, tablets, personal computers, application specific custom gateways, audio devices, wearable activity monitors, automotive consoles, other devices described in this section or elsewhere in this specification, and other available devices for communicating data.

Fig. 13 is a schematic diagram of an embodiment of a system and method 3000 of wearable ECG and/or medical sensors 3002 with transmission capabilities that is very similar to the systems and/or methods described above with reference to fig. 11 and 12. Fig. 13 differs from fig. 11 and 12 in that fig. 13 shows alternative sensor channels 3004, 3006 that produce alternative outputs and/or extractions 3008 of the feature 3010. The collection of other data channels can be used to further enhance the features of the ECG extraction. Data from the optional sensor channels may be sent in its entirety or the data channels of a particular feature 3010 may be extracted 3008. In some embodiments, the optional data channel may record galvanic skin response/impedance. This data may indicate to the sensor 3002 whether the wire is on or off based on a preset threshold and a built-in hysteresis, for example, by a Boolean algorithm indicating whether the wire is on/off during a given period of time. This information may further be used to remove the non-contact time of the device with the body from the analysis. In some embodiments, collecting more fine-grained impedance data points may provide insight into changes in patient activity levels due to changes in sweat levels, in addition to on/off indication boolean. In an embodiment, the optional sensor data channel may be from an accelerometer. Such a device may provide free fall detection by on-board algorithms to detect free fall, i.e., an indication that the patient has fallen down suddenly due to syncope caused by arrhythmia. Further, the magnitude of the acceleration detected by the accelerometer may be used to detect sleep cycles, activity levels, types of activity, and/or likelihood of motion artifacts, all of which may be associated with prevalence of some rhythm types. In particular embodiments, the raw accelerometer values may be used to determine the body position at a given reference point (e.g., whether the patient is upright when first mounted). In addition, in addition to providing more insight into the type of activity, changes in the orientation of the accelerometer can be used to distinguish between clinically relevant morphological changes and non-clinically relevant changes.

In some embodiments, the optional data channel may be provided by a pulse oximeter. For example, a pulse oximeter may produce a photoplethysmogram (PPG). PPG can provide an alternative source for R-peak position or cross-checking as R-peak detection of ECG circuits. Further, the PPG data channel may be combined with multiple PPG/BioZ channels to output a confidence in the R-peak detection confidence level. In a further embodiment, spO 2/perfusion by pulse oximeter may provide a further clinical indication of severe arrhythmia. In some embodiments, the optional sensor channel may involve bioimpedance that may be used to determine the location of the heartbeat and/or as an optional source of R-peak data. In some embodiments, temperature data may be provided through an optional sensor channel. Such data may be used in conjunction with other activity metrics to identify activity type, level, and/or sleep. In some embodiments, the optional data channel may provide information from a clock, such as the time of day or an indication of day or night. In some embodiments, the optional data channel may be provided by a microphone/stethoscope to provide audible recording of the heartbeat. Finally, an optional data channel may be provided by a flexion or bending sensor which may allow identification of motion artifacts.

Fig. 14 is a schematic diagram of an embodiment of a system and method 4000 having wearable ECG and/or medical sensors 4002 with transmission capabilities that is very similar to the systems and/or methods described above with reference to fig. 11-13. Fig. 14 differs from fig. 11-13 in that the embodiment of fig. 14 includes an additional data filter. In some embodiments, processing device 4004 may also filter rhythms 4006 identified by rhythm inference system 4008 by applying filter criteria that may be derived from multiple sources. For example, the filter criteria may be derived from: the physician's interest in reporting a particular rhythm type in time, the physician's interest in observing rhythms similar to those observed previously (e.g., if multiple 3 second pauses have been reported to the physician, the threshold of interest is changed to 4 or 5 seconds). In some embodiments, filtering may include automatic filtering that limits repeated retrieval of similar rhythm types and durations. Automatic filtering may limit lower positive predicted events, e.g., repeated retrieval of records with high levels of motion artifacts, where the positive prediction rate of the rhythm inference engine is lower, and may allow automatic filtering of subsequent requests. Such an approach may utilize the confidence interval assigned by the rhythm inference system and the tracking history of the rhythm inference system's positive predictability for a given monitoring phase.

Fig. 15 is a schematic diagram of an embodiment of a system 5000 for a consumer wearable device without full ECG detection, having some similarities to the medical sensors of fig. 10-14. The sensor 5002 need not be a medical-grade ECG sensor, but only allows for detection of beats. In an embodiment, sensor 5002 can continuously sense the data channel from which the location of the heartbeat can be derived. Possible data sources include: PPG (optionally with multiple channels to improve accuracy), bio-impedance, and ECG that are not fully implemented due to insufficient signal quality compared to the sensors of fig. 10-14. Similar to the apparatus of fig. 10 to 14, for example, the following features may be extracted from the signal: r peak position, R peak overflow flag, saturation flag, respiration rate, P/T wave position, R peak amplitude (or proxy), or ECG signal amplitude proxy. Data extraction may be performed via any of the methods described in this section or elsewhere in this specification. In some embodiments, other data channels are collected to improve confidence in the rhythm assessment. The consumer device system 5000 may further transmit and process data by any of the methods described in this section herein or elsewhere in this specification. Based on these determinations, the results of the rhythm analysis may be sent to the user.

The consumer device system 5000 without full ECG detection advantageously enables arrhythmia analysis using heart rate sensors available to the consumer, thereby reducing cost and increasing device usability. This therefore makes possible arrhythmia screening for a larger population, including via non-prescription screening.

Fig. 16 is a schematic diagram of an embodiment of an ECG monitoring system 6000 with symptom transmission. Such a system would involve wearable ECG sensors similar to those described with reference to fig. 1-14. As described above, such sensors continuously sense and record ECGs. Each symptom trigger of the patient may initiate the transfer of a strip of ECG data. The data bands may vary in time location as well as duration, and may be event-centric. In some embodiments, the data bands may be biased for a period of time before symptom triggering, or they may be biased for a period of time after symptom triggering. The data bands may have a duration as short as a few heartbeats (-5 to 10 seconds), or 60 to 90 seconds, or longer (-5 to 20 minutes). In embodiments, the data band duration may be dynamically changed programmatically based on clinical needs or automatically adjusted based on patient trigger frequency. The ECG data strip may be transmitted by any of the means disclosed in this section of this document or elsewhere in this specification. If the transfer is effected without patient intervention (e.g., not NFC or wired transfer), the data transfer may be initiated automatically and timely without further patient interaction beyond the symptom trigger.

The location for data band analysis may vary. For example, local analysis of a patient may occur on a smartphone, tablet, or PC. Alternatively, the local analysis of the clinic may occur on a server or other processing device, or the local analysis of the ECG analysis service provider may occur on a server or other processing device. Finally, in embodiments, the analysis may occur using cloud-based distributed processing resources. In some embodiments, a report may be provided for each symptom ECG data strip, however, if the symptom ECG data strip is not determined to be clinically meaningful, no report may be provided. In some cases, available reports may be made, but the notifications issued to the user may be limited to situations with particular clinical relevance. Providing this option may limit the demand for user time.

In some embodiments, the report may be delivered in various ways. For example, the report may be delivered by: through a website, through a smartphone, tablet or PC application, through an electronic health record (EMR/EHR) system having interoperability and integrated into multiple provider systems, or through automated messaging such as email, SMS, application-based messaging, and the like. The recipient of the report may vary, in some applications the report recipient may be a patient user, while in other applications the report recipient may be a clinician.

In certain embodiments, when monitoring is complete, the patient removes the device and sends a complete continuous ECG recording to the data processing location. The method of transmission may vary, for example, it may be transmitted by physical transfer of the entire device, such as by mail or by bringing the device to a prescribing clinic, or it may be transmitted via local download of data and subsequent download to a data processing location. In some cases, the patient may not wait to remove the device before sending the partial segments of the continuous ECG recording, which would be accomplished by a transmission method such as NFC or ultra-low power wireless data transfer that does not require a removal device. As with the symptomatic ECG analysis described above, the data processing location may vary.

Fig. 17 is a schematic diagram of an embodiment of an ECG monitoring system 7000 with both symptomatic and asymptomatic transmission. The wearable sensor is similar to the sensors described in this section herein or elsewhere in this specification. However, in an embodiment, each asymptomatic trigger initiates the transfer of a strip of ECG data as described above. Further features of the physiological monitoring devices described in this section or elsewhere in this specification facilitate this implementation. As with the other embodiments described above, the high fidelity ECG recording enabled by the designs described in this section or elsewhere in this specification allows for increased confidence in the accuracy of feature extraction.

Computing system and method

In some embodiments, the systems, tools, and methods of using the same described above enable interactivity and data collection to be performed by the computing system 13000. Fig. 18 is a block diagram illustrating an embodiment in which a computing system 13000 communicates with a network 13002 and various external computing systems 13004, such as a wearable system 13005, a gateway device 13006, the external computing systems 13004 also communicating with the network 13002. Computing system 13000 can be used to implement the systems and methods described herein. While the external systems 13004 are shown as groups, it is recognized that each of the systems can be external and/or remotely located from each other.

In some embodiments, computing system 13000 includes one or more computing devices, such as: such as a server, laptop computer, mobile device (e.g., smartphone, smartwatch, tablet, personal digital assistant), kiosk, automobile console, or media player. In one embodiment, computing device 13000 includes one or more Central Processing Units (CPUs) 13105, which may each include a conventional microprocessor or a special purpose microprocessor. The computing device 13000 further includes one or more memories 13130, such as a Random Access Memory (RAM) for temporarily storing information, one or more Read Only Memories (ROM) for permanently storing information, and one or more mass storage devices 13120, such as a hard disk, floppy disk, solid state drive, or optical media storage device. In some embodiments, a processing device, cloud server, or gateway device may be implemented as computing system 1300. In one embodiment, the modules of computing system 13000 are connected to the computer using a standard-based bus system. In various embodiments, the standards-based bus system may be implemented in, for example: peripheral Component Interconnect (PCI), micro-channel, small Computer System Interface (SCSI), industry Standard Architecture (ISA), and Extended ISA (EISA) architectures. In addition, the functionality provided for in the components and modules of computing device 13000 can be combined into fewer components and modules or further divided into additional components and modules.

The computing device 13000 may be controlled and coordinated by operating system software such as: iOS, windows XP, windows Vista, windows 7, windows 8, windows 10, windows server, embedded Windows, unix, linux, ubuntu Linux), sunOS, solaris, blackberry OS, android, or other operating systems. In the Macintosh system, the operating system can be any available operating system such as MAC OS X. In other embodiments, computing device 13000 can be controlled by a dedicated operating system. In addition, conventional operating systems control and schedule computer processes for execution, perform memory management, provide file systems, networks, I/O services, provide user interfaces such as Graphical User Interfaces (GUIs), and the like.

The exemplary computing device 13000 may include one or more I/O interfaces and devices 13110, such as a touchpad or a touch screen, but may also include a keyboard, mouse, and printer. In one embodiment, the I/O interfaces and devices 13110 include one or more display devices (such as a touch screen or monitor) that allow visual presentation of data to a user. More particularly, the display device may provide, for example, presentation of a GUI, application software data, and multimedia presentation. The computing system 13000 can also include one or more multimedia devices 13140, such as, for example, a camera, speakers, a video card, a graphics accelerator, and a microphone.

In one embodiment of the computing system and application tool, the I/O interfaces and devices 13110 may provide a communications interface to various external devices. In one embodiment, the computing device 13000 is electrically coupled to the network 13002 by a wired communication link 13115, a wireless communication link 13115, or a combined wired and wireless communication link 13115, the network 13002 including, for example, one or more of a local area network, a wide area network, and/or the internet. The network 13002 may communicate with various sensors, computing devices, and/or other electronic devices via wired or wireless communication links.

In some embodiments, the filter criteria, signals, and data are processed by an application tool rhythm inference module in accordance with the methods and systems described herein and may be provided to the computing system 13000 from one or more data sources 13010 via the network 13002. The data sources may include one or more internal and/or external databases, data sources, and physical data stores. The data source 13010, external computing system 13004, and rhythm interface module 13190 may include a database for storing data (e.g., characteristic data, raw signal data, patient data) according to the systems and methods described above, a database for storing data that has been processed (e.g., data to be transmitted to a sensor, data to be transmitted to a clinician) according to the systems and methods described above. In one embodiment of fig. 19, in some embodiments, the sensor data 14050 can store data received from sensors, data received from clinicians, and the like. In some embodiments, the rules database 14060 may store parameters that establish thresholds for analyzing feature dataSuch as instructions, preferences, profiles (profiles). In some embodiments, one or more of the databases or data sources may use a relational database such as Sybase, oracle, codebase, mySQL, SQLite, and

SQL servers, as well as other types of databases such as, for example, flat file databases, entity relational databases, object oriented databases, noSQL databases, and/or record-based databases.

In one embodiment, the computing system includes a rhythm interface module 13190, which may be stored in mass storage 13120 as executable software code executed by CPU 13105. Rhythm interface module 13190 may have feature module 14010, optional data module 14020, inference module 14030, feedback module 14040, sensor data database 14050, and rules database 14060. For example, these modules may include components such as software components, object-oriented software components, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables. These modules are also configured to perform the processes disclosed herein, including the processes described in some embodiments with respect to fig. 10-17.

Generally, the word "module" as used herein refers to logic contained in hardware or firmware or a set of software instructions written in a programming language such as Python, java, lua, C, and/or C + + that may have entry and exit points. The software modules may be compiled and linked into executable programs, installed in dynamically linked libraries, or written in an interpreted programming language such as, for example, BASIC, perl, or Python. It will be appreciated that software modules may be invoked from other modules or from themselves, and/or may be invoked in response to detected events or interrupts. Software modules configured to execute on a computing device may be provided on a computer readable medium such as a compact disc, digital video disc, flash drive, or any other tangible medium. Such software code can be stored in part or in whole on a memory device executing a computing device, such as computing system 13000, for execution by the computing device. The software instructions may be embedded in firmware, such as an EPROM. It will be further understood that a hardware module may be composed of connected logic units such as gates and flip-flops and/or may be composed of programmable units such as programmable gate arrays or processors. The block diagrams disclosed herein may be implemented as modules. The modules described herein may be implemented as software modules, but may be represented as hardware or firmware. Generally, modules described herein refer to logical modules that may be combined with other modules or divided into sub-modules, regardless of their physical configuration or storage.

Each of the processes, methods, and algorithms described in the preceding sections may be embodied in, and fully or partially automated by, code modules executed by one or more computer systems or computer processors comprising computer hardware. The code modules may be stored on any type of non-transitory computer-readable medium or computer storage device, such as a hard disk, solid state memory, optical disk, and the like. The systems and modules may also be transmitted as generated data signals (e.g., as part of a carrier wave or other analog or digital propagated signal) on a variety of computer-readable transmission media, including wireless-based media and wired/cable-based media, and may take a variety of forms (e.g., as part of a single analog signal or multiple analog signals, or as part of multiple discrete digital packets or frames). The processes and algorithms may be implemented partially or completely in application-specific circuitry. The results of the disclosed processes and process steps can be stored persistently or otherwise in any type of non-transitory computer memory, such as, for example, volatile memory or non-volatile memory.

The various features and processes described above may be used independently of one another or may be combined in various ways. All possible combinations and sub-combinations are intended to fall within the scope of the present disclosure. In addition, some method or process blocks may be omitted in some implementations. The methods and processes described herein are also not limited to any particular sequence, and the blocks or states associated therewith may be performed in other sequences as appropriate. For example, the blocks or states described may be performed in an order other than the order specifically disclosed, or multiple blocks or states may be combined in a single block or state. The exemplary blocks or states may be performed in series, in parallel, or in some other manner. Blocks or states may be added to or removed from the disclosed exemplary embodiments. The example systems and components described herein may be configured differently than described. For example, elements may be added, removed, or rearranged compared to the disclosed exemplary embodiments.

Conditional language, such as "may," "might," "perhaps" or "may" is generally intended to convey that some embodiments include some features, elements and/or steps, while other embodiments do not include some, unless expressly stated otherwise or understood otherwise in the context of usage. Thus, such conditional language is not generally intended to imply that features, elements and/or steps are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without user input or prompting, whether these features, elements and/or steps are included or are to be performed in any particular embodiment. The term "including" means "including but not limited to". The term "or" means "and/or".

Any process descriptions, elements or blocks in flow diagrams or block diagrams described herein and/or in the drawings should be understood as potentially representing modules, segments or portions of code which include one or more executable instructions for implementing specific logical functions or steps in the process. Alternative implementations are included within the scope of the embodiments described herein in which elements or functions may be deleted, performed in an order other than that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those skilled in the art.

All of the methods and processes described above can be at least partially included in and partially or fully automated by software code modules executed by one or more computers. For example, the methods described herein may be performed by a computing system and/or any other suitable computing device. The method may be performed on a computing device in response to execution of software instructions or other executable code read from a tangible computer-readable medium. The tangible computer readable medium is a data storage device that can store data that is readable by a computer system. Examples of a computer readable medium include read-only memory, random-access memory, other volatile or non-volatile memory devices, CD-ROMs, magnetic tape, flash drives, and optical data storage devices.

It should be emphasized that many variations and modifications may be made to the above-described embodiments, the elements of which are to be understood as being among other acceptable examples. All such modifications and variations are intended to be included herein within the scope of this disclosure. The foregoing description details some embodiments. However, it will be appreciated that no matter how detailed the foregoing appears in text, the systems and methods can be practiced in many ways. For example, features from one embodiment may be used with features from a different embodiment. As also noted above, it should be noted that the use of particular terminology when describing certain features or aspects of the systems and methods should not be taken to imply that the terminology is being re-defined herein to be restricted to including any specific characteristics of the features or aspects of the systems and methods with which that terminology is associated.

Various embodiments of physiological monitoring devices, methods, and systems are disclosed herein. These various embodiments may be used alone or in combination, and various changes in the respective features of the embodiments may be changed without departing from the scope of the present invention. For example, the order of the various method steps may be changed in some cases, and/or one or more optional features may be added to or removed from the described apparatus. Therefore, the description of the embodiments provided above should not be construed as unduly limiting the scope of the present invention as set forth in the claims.

Various modifications to the embodiments described in this disclosure may be made, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the scope of the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the present disclosure, the principles and novel features disclosed herein.

In the context of separate embodiments, some features described in this specification may also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features of a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particular order, these operations need not be performed in the particular order shown or in sequential order, or all of the illustrated operations may be performed, to achieve desirable results. Further, the figures may schematically depict another exemplary process in the form of a flow diagram. However, other operations not depicted may be incorporated into the exemplary process shown schematically. For example, one or more additional operations may be performed before, after, concurrently with, or between any of the illustrated operations. Further, the separation of various system components in the embodiments described above should not be construed as requiring such separation in all embodiments. Additionally, other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results.

Claims (32)

1. A wearable device for monitoring data, the device comprising:

a sensor configured to detect a cardiac signal from the mammal;

a processor configured to use a machine learning model to extract a machine learning output from the cardiac signal, wherein the machine learning output includes less data than the cardiac signal;

a transmitter configured to transmit the machine learning output to a computing device configured to determine an atrial fibrillation burden from the machine learning output;

wherein the computing device is configured to provide a report including a likelihood of a past occurrence of an atrial fibrillation event.

2. The wearable device of claim 1, wherein the atrial fibrillation burden comprises the time a mammal spends in atrial fibrillation per day.

3. The wearable device of claim 1, wherein the atrial fibrillation burden comprises the time the mammal spends in atrial fibrillation per minute.

4. The wearable device of claim 1, wherein the atrial fibrillation burden comprises the time the mammal spends in atrial fibrillation during sleep and during wakefulness.

5. The wearable device of claim 1, wherein the report comprises a histogram of atrial fibrillation burden.

6. The wearable device of claim 1, further comprising an accelerometer configured to measure movement of a mammal.

7. The wearable device of claim 6, wherein the atrial fibrillation burden comprises the time a mammal spends in atrial fibrillation during locomotion.

8. The wearable device of claim 7, wherein the movement of the mammal comprises a first stage of movement and a second stage of movement.

9. The wearable device of claim 1, wherein the report comprises a 14 day monitoring period.

10. The wearable device of claim 1, wherein the computing device infers a plurality of most likely heart rhythms by filtering the machine learning output according to a predetermined threshold.

11. The wearable device of claim 1, wherein the sensor and the processor are included within a chest strap.

12. The wearable device of claim 1, wherein the sensor and the processor are contained within a watch configured to be worn on a wrist of a person.

13. The wearable device of claim 1, wherein the sensor and the processor are included within a wearable fitness band.

14. A wearable device for monitoring data, the device comprising:

a sensor configured to detect a cardiac signal from the mammal;

a processor configured to use a machine learning model to extract a machine learning output from the cardiac signal, the machine learning output comprising less data than the cardiac signal; and

a transmitter configured to transmit the machine learning output to a computing device configured to determine an arrhythmia burden from the machine learning output;

wherein the computing device is configured to provide a report including a likelihood of a past occurrence of an arrhythmia.

15. The wearable device of claim 14, wherein the arrhythmia comprises ventricular tachycardia.

16. The wearable device of claim 14, wherein the arrhythmia comprises supraventricular tachycardia.

17. The wearable device of claim 14, wherein the arrhythmia comprises ectopy.

18. The wearable device of claim 14, wherein the arrhythmia comprises ventricular fibrillation.

19. The wearable device of claim 14, wherein the arrhythmia comprises a prolonged pause.

20. The wearable device of claim 14, wherein the report comprises a histogram of arrhythmia burden.

21. A wearable device for monitoring data, the device comprising:

a sensor configured to detect a cardiac signal from the mammal;

a processor configured to use a machine learning model to extract a machine learning output from the cardiac signal, wherein the machine learning output includes less data than the cardiac signal,

a transmitter configured to transmit the machine learning output to a computing device configured to infer a likelihood of a past occurrence of an arrhythmia from the machine learning output;

wherein the computing device is configured to provide a report including a likelihood of a past occurrence of an arrhythmia.

22. The device of claim 21, wherein the computing device is a server.

23. The device of claim 21, wherein the computing device is a smartphone.

24. The device of claim 22, wherein the computing device communicates with the transmitter through a smartphone intermediary.

25. The apparatus of claim 21, wherein the report includes an indication of the presence of atrial fibrillation.

26. The apparatus of claim 25, wherein the indication of the presence of atrial fibrillation comprises detecting at least 1000 beats.

27. The device of claim 21, wherein the computing device infers a plurality of most likely cardiac rhythms by filtering the machine learning output according to a predetermined threshold.

28. The device of claim 21, wherein the processor is further configured to estimate a signal amplitude from the cardiac signal, the transmitter further configured to transmit the signal amplitude to the computing device.

29. The device of claim 21, wherein the processor is further configured to estimate a noise level from the cardiac signal, the transmitter being further configured to transmit the noise level to the computing device.

30. The device of claim 21, wherein the processor is configured to collect a secondary signal and transmit the secondary signal to the computing device.

31. The apparatus of claim 30, wherein the secondary signal is accelerometer data.

32. The apparatus of claim 30, wherein the secondary signal is electrode contact quality data.

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